Overview

Brought to you by YData

Dataset statistics

Number of variables39
Number of observations56307
Missing cells0
Missing cells (%)0.0%
Duplicate rows1564
Duplicate rows (%)2.8%
Total size in memory16.8 MiB
Average record size in memory312.0 B

Variable types

Categorical33
Numeric4
Text2

Alerts

FechaCorte has constant value "20241231" Constant
FechaActualizacion has constant value "20250131" Constant
Dataset has 1564 (2.8%) duplicate rowsDuplicates
Año de Independización/Ingreso is highly overall correlated with Año desmovilización and 4 other fieldsHigh correlation
Año desmovilización is highly overall correlated with Año de Independización/Ingreso and 3 other fieldsHigh correlation
BeneficioPDT is highly overall correlated with Año desmovilización and 3 other fieldsHigh correlation
BeneficioTRV is highly overall correlated with Año desmovilización and 3 other fieldsHigh correlation
Departamento de residencia is highly overall correlated with Año de Independización/Ingreso and 3 other fieldsHigh correlation
Departamento de residencia descripción is highly overall correlated with Año de Independización/Ingreso and 3 other fieldsHigh correlation
DesagregadoDesembolsoBIE is highly overall correlated with BeneficioPDT and 8 other fieldsHigh correlation
Desembolso BIE is highly overall correlated with DesagregadoDesembolsoBIE and 6 other fieldsHigh correlation
Estado ISUN is highly overall correlated with DesagregadoDesembolsoBIE and 2 other fieldsHigh correlation
Estado de la vinculación ASS is highly overall correlated with Desembolso BIE and 3 other fieldsHigh correlation
Ex Grupo is highly overall correlated with Tipo de DesmovilizaciónHigh correlation
Ingresó/No ingresó is highly overall correlated with Año de Independización/Ingreso and 3 other fieldsHigh correlation
Nivel Educativo is highly overall correlated with Posee Censo de Habitabilidad?High correlation
N° de Hijos is highly overall correlated with Posee Censo de Familia? and 4 other fieldsHigh correlation
OcupacionEconomica is highly overall correlated with Posee Censo de Familia? and 2 other fieldsHigh correlation
Posee Censo de Familia? is highly overall correlated with DesagregadoDesembolsoBIE and 12 other fieldsHigh correlation
Posee Censo de Habitabilidad? is highly overall correlated with DesagregadoDesembolsoBIE and 13 other fieldsHigh correlation
Posee Cónyuge o Compañero(a)? is highly overall correlated with N° de Hijos and 3 other fieldsHigh correlation
Posee Serv. Públicos Básicos is highly overall correlated with N° de Hijos and 7 other fieldsHigh correlation
Posee Servicio Social? is highly overall correlated with DesagregadoDesembolsoBIE and 6 other fieldsHigh correlation
Régimen de tenencia Vivienda is highly overall correlated with Posee Censo de Familia? and 2 other fieldsHigh correlation
Situación Final frente al proceso is highly overall correlated with Año de Independización/Ingreso and 8 other fieldsHigh correlation
Tipo de ASS Vinculada is highly overall correlated with Desembolso BIE and 4 other fieldsHigh correlation
Tipo de BIE Accedido is highly overall correlated with DesagregadoDesembolsoBIE and 2 other fieldsHigh correlation
Tipo de Desmovilización is highly overall correlated with Año desmovilización and 3 other fieldsHigh correlation
Tipo de Vivienda is highly overall correlated with Posee Censo de Familia? and 2 other fieldsHigh correlation
Total Integrantes grupo familiar is highly overall correlated with N° de Hijos and 2 other fieldsHigh correlation
Ex Grupo is highly imbalanced (61.4%) Imbalance
Ingresó/No ingresó is highly imbalanced (72.6%) Imbalance
BeneficioTRV is highly imbalanced (71.5%) Imbalance
BeneficioFA is highly imbalanced (94.0%) Imbalance
BeneficioFPT is highly imbalanced (97.2%) Imbalance
BeneficioPDT is highly imbalanced (70.3%) Imbalance
Estado de la vinculación ASS is highly imbalanced (56.7%) Imbalance
Clasificación Componente Específico is highly imbalanced (98.4%) Imbalance
N° de Hijos has 17296 (30.7%) zeros Zeros

Reproduction

Analysis started2025-02-08 12:31:37.502538
Analysis finished2025-02-08 12:31:57.506224
Duration20 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Tipo de Desmovilización
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
Colectiva
30879 
Individual
25428 

Length

Max length10
Median length9
Mean length9.4515957
Min length9

Characters and Unicode

Total characters532191
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowColectiva
3rd rowColectiva
4th rowIndividual
5th rowIndividual

Common Values

ValueCountFrequency (%)
Colectiva 30879
54.8%
Individual 25428
45.2%

Length

2025-02-08T12:31:57.632326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:31:57.743090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
colectiva 30879
54.8%
individual 25428
45.2%

Most occurring characters

ValueCountFrequency (%)
i 81735
15.4%
l 56307
10.6%
v 56307
10.6%
a 56307
10.6%
d 50856
9.6%
C 30879
 
5.8%
o 30879
 
5.8%
e 30879
 
5.8%
c 30879
 
5.8%
t 30879
 
5.8%
Other values (3) 76284
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 532191
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 81735
15.4%
l 56307
10.6%
v 56307
10.6%
a 56307
10.6%
d 50856
9.6%
C 30879
 
5.8%
o 30879
 
5.8%
e 30879
 
5.8%
c 30879
 
5.8%
t 30879
 
5.8%
Other values (3) 76284
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 532191
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 81735
15.4%
l 56307
10.6%
v 56307
10.6%
a 56307
10.6%
d 50856
9.6%
C 30879
 
5.8%
o 30879
 
5.8%
e 30879
 
5.8%
c 30879
 
5.8%
t 30879
 
5.8%
Other values (3) 76284
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 532191
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 81735
15.4%
l 56307
10.6%
v 56307
10.6%
a 56307
10.6%
d 50856
9.6%
C 30879
 
5.8%
o 30879
 
5.8%
e 30879
 
5.8%
c 30879
 
5.8%
t 30879
 
5.8%
Other values (3) 76284
14.3%

Ex Grupo
Categorical

High correlation  Imbalance 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
AUC
33330 
FARC
17582 
ELN
4696 
ERP
 
170
ERG
 
140
Other values (7)
 
389

Length

Max length19
Median length3
Mean length3.3587831
Min length3

Characters and Unicode

Total characters189123
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowELN
2nd rowAUC
3rd rowAUC
4th rowFARC
5th rowFARC

Common Values

ValueCountFrequency (%)
AUC 33330
59.2%
FARC 17582
31.2%
ELN 4696
 
8.3%
ERP 170
 
0.3%
ERG 140
 
0.2%
EPL 135
 
0.2%
GAO Residual 87
 
0.2%
SIN DATO MINDEFENSA 81
 
0.1%
SIN DATO 73
 
0.1%
GAO Clan del Golfo 10
 
< 0.1%
Other values (2) 3
 
< 0.1%

Length

2025-02-08T12:31:58.160221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
auc 33330
58.8%
farc 17582
31.0%
eln 4696
 
8.3%
erp 170
 
0.3%
sin 154
 
0.3%
dato 154
 
0.3%
erg 140
 
0.2%
epl 135
 
0.2%
gao 100
 
0.2%
residual 87
 
0.2%
Other values (6) 114
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A 51247
27.1%
C 50924
26.9%
U 33330
17.6%
R 17979
 
9.5%
F 17663
 
9.3%
E 5303
 
2.8%
N 5012
 
2.7%
L 4831
 
2.6%
355
 
0.2%
P 306
 
0.2%
Other values (19) 2173
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 189123
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 51247
27.1%
C 50924
26.9%
U 33330
17.6%
R 17979
 
9.5%
F 17663
 
9.3%
E 5303
 
2.8%
N 5012
 
2.7%
L 4831
 
2.6%
355
 
0.2%
P 306
 
0.2%
Other values (19) 2173
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 189123
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 51247
27.1%
C 50924
26.9%
U 33330
17.6%
R 17979
 
9.5%
F 17663
 
9.3%
E 5303
 
2.8%
N 5012
 
2.7%
L 4831
 
2.6%
355
 
0.2%
P 306
 
0.2%
Other values (19) 2173
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 189123
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 51247
27.1%
C 50924
26.9%
U 33330
17.6%
R 17979
 
9.5%
F 17663
 
9.3%
E 5303
 
2.8%
N 5012
 
2.7%
L 4831
 
2.6%
355
 
0.2%
P 306
 
0.2%
Other values (19) 2173
 
1.1%

Año desmovilización
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.1369
Minimum2001
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size440.0 KiB
2025-02-08T12:31:58.307560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2004
Q12005
median2006
Q32008
95-th percentile2016
Maximum2024
Range23
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.57866
Coefficient of variation (CV)0.0017829676
Kurtosis3.7783489
Mean2007.1369
Median Absolute Deviation (MAD)1
Skewness1.9346404
Sum1.1301586 × 108
Variance12.806808
MonotonicityNot monotonic
2025-02-08T12:31:58.459957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2006 19591
34.8%
2005 12019
21.3%
2004 4369
 
7.8%
2008 2858
 
5.1%
2007 2729
 
4.8%
2009 2548
 
4.5%
2003 2439
 
4.3%
2010 2009
 
3.6%
2011 1214
 
2.2%
2013 1059
 
1.9%
Other values (14) 5472
 
9.7%
ValueCountFrequency (%)
2001 2
 
< 0.1%
2002 9
 
< 0.1%
2003 2439
 
4.3%
2004 4369
 
7.8%
2005 12019
21.3%
2006 19591
34.8%
2007 2729
 
4.8%
2008 2858
 
5.1%
2009 2548
 
4.5%
2010 2009
 
3.6%
ValueCountFrequency (%)
2024 90
 
0.2%
2023 79
 
0.1%
2022 137
 
0.2%
2021 193
 
0.3%
2020 203
 
0.4%
2019 244
 
0.4%
2018 404
0.7%
2017 811
1.4%
2016 667
1.2%
2015 767
1.4%

Ingresó/No ingresó
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
53660 
No
 
2647

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters112614
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
53660
95.3%
No 2647
 
4.7%

Length

2025-02-08T12:31:58.606801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:31:58.696588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
53660
95.3%
no 2647
 
4.7%

Most occurring characters

ValueCountFrequency (%)
S 53660
47.6%
í 53660
47.6%
N 2647
 
2.4%
o 2647
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 53660
47.6%
í 53660
47.6%
N 2647
 
2.4%
o 2647
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 53660
47.6%
í 53660
47.6%
N 2647
 
2.4%
o 2647
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 53660
47.6%
í 53660
47.6%
N 2647
 
2.4%
o 2647
 
2.4%

Año de Independización/Ingreso
Real number (ℝ)

High correlation 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1913.6676
Minimum-1
Maximum2024
Zeros0
Zeros (%)0.0%
Negative2647
Negative (%)4.7%
Memory size440.0 KiB
2025-02-08T12:31:58.799507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile2003
Q12005
median2006
Q32009
95-th percentile2017
Maximum2024
Range2025
Interquartile range (IQR)4

Descriptive statistics

Standard deviation425.27351
Coefficient of variation (CV)0.22222956
Kurtosis16.319458
Mean1913.6676
Median Absolute Deviation (MAD)1
Skewness-4.2798209
Sum1.0775288 × 108
Variance180857.56
MonotonicityNot monotonic
2025-02-08T12:31:58.965246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2006 16796
29.8%
2005 10450
18.6%
2007 4402
 
7.8%
2008 2971
 
5.3%
2009 2650
 
4.7%
-1 2647
 
4.7%
2011 2385
 
4.2%
2010 2259
 
4.0%
2004 1935
 
3.4%
2017 1212
 
2.2%
Other values (13) 8600
15.3%
ValueCountFrequency (%)
-1 2647
 
4.7%
2003 735
 
1.3%
2004 1935
 
3.4%
2005 10450
18.6%
2006 16796
29.8%
2007 4402
 
7.8%
2008 2971
 
5.3%
2009 2650
 
4.7%
2010 2259
 
4.0%
2011 2385
 
4.2%
ValueCountFrequency (%)
2024 184
 
0.3%
2023 144
 
0.3%
2022 218
 
0.4%
2021 326
 
0.6%
2020 353
 
0.6%
2019 431
 
0.8%
2018 860
1.5%
2017 1212
2.2%
2016 1072
1.9%
2015 983
1.7%

Grupo Etario
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
Entre 41 y 60 años
34548 
Entre 26 y 40 años
18340 
Mayor de 60 años
 
2386
Entre 18 y 25 años
 
973
<No Registra>
 
60

Length

Max length18
Median length18
Mean length17.909922
Min length13

Characters and Unicode

Total characters1008454
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMayor de 60 años
2nd rowMayor de 60 años
3rd rowEntre 41 y 60 años
4th rowMayor de 60 años
5th rowMayor de 60 años

Common Values

ValueCountFrequency (%)
Entre 41 y 60 años 34548
61.4%
Entre 26 y 40 años 18340
32.6%
Mayor de 60 años 2386
 
4.2%
Entre 18 y 25 años 973
 
1.7%
<No Registra> 60
 
0.1%

Length

2025-02-08T12:31:59.140794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:31:59.252841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
años 56247
20.2%
entre 53861
19.3%
y 53861
19.3%
60 36934
13.2%
41 34548
12.4%
26 18340
 
6.6%
40 18340
 
6.6%
mayor 2386
 
0.9%
de 2386
 
0.9%
18 973
 
0.3%
Other values (3) 1093
 
0.4%

Most occurring characters

ValueCountFrequency (%)
222662
22.1%
o 58693
 
5.8%
a 58693
 
5.8%
r 56307
 
5.6%
e 56307
 
5.6%
s 56307
 
5.6%
ñ 56247
 
5.6%
y 56247
 
5.6%
6 55274
 
5.5%
0 55274
 
5.5%
Other values (16) 276443
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1008454
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
222662
22.1%
o 58693
 
5.8%
a 58693
 
5.8%
r 56307
 
5.6%
e 56307
 
5.6%
s 56307
 
5.6%
ñ 56247
 
5.6%
y 56247
 
5.6%
6 55274
 
5.5%
0 55274
 
5.5%
Other values (16) 276443
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1008454
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
222662
22.1%
o 58693
 
5.8%
a 58693
 
5.8%
r 56307
 
5.6%
e 56307
 
5.6%
s 56307
 
5.6%
ñ 56247
 
5.6%
y 56247
 
5.6%
6 55274
 
5.5%
0 55274
 
5.5%
Other values (16) 276443
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1008454
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
222662
22.1%
o 58693
 
5.8%
a 58693
 
5.8%
r 56307
 
5.6%
e 56307
 
5.6%
s 56307
 
5.6%
ñ 56247
 
5.6%
y 56247
 
5.6%
6 55274
 
5.5%
0 55274
 
5.5%
Other values (16) 276443
27.4%

Sexo
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
MASCULINO
32816 
Masculino
15969 
FEMENINO
5259 
Femenino
 
2263

Length

Max length9
Median length9
Mean length8.8664109
Min length8

Characters and Unicode

Total characters499241
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMasculino
2nd rowMasculino
3rd rowMASCULINO
4th rowMasculino
5th rowMasculino

Common Values

ValueCountFrequency (%)
MASCULINO 32816
58.3%
Masculino 15969
28.4%
FEMENINO 5259
 
9.3%
Femenino 2263
 
4.0%

Length

2025-02-08T12:31:59.405567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:31:59.513153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
masculino 48785
86.6%
femenino 7522
 
13.4%

Most occurring characters

ValueCountFrequency (%)
M 54044
 
10.8%
N 43334
 
8.7%
I 38075
 
7.6%
O 38075
 
7.6%
S 32816
 
6.6%
C 32816
 
6.6%
U 32816
 
6.6%
L 32816
 
6.6%
A 32816
 
6.6%
n 20495
 
4.1%
Other values (11) 141138
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 499241
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 54044
 
10.8%
N 43334
 
8.7%
I 38075
 
7.6%
O 38075
 
7.6%
S 32816
 
6.6%
C 32816
 
6.6%
U 32816
 
6.6%
L 32816
 
6.6%
A 32816
 
6.6%
n 20495
 
4.1%
Other values (11) 141138
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 499241
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 54044
 
10.8%
N 43334
 
8.7%
I 38075
 
7.6%
O 38075
 
7.6%
S 32816
 
6.6%
C 32816
 
6.6%
U 32816
 
6.6%
L 32816
 
6.6%
A 32816
 
6.6%
n 20495
 
4.1%
Other values (11) 141138
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 499241
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 54044
 
10.8%
N 43334
 
8.7%
I 38075
 
7.6%
O 38075
 
7.6%
S 32816
 
6.6%
C 32816
 
6.6%
U 32816
 
6.6%
L 32816
 
6.6%
A 32816
 
6.6%
n 20495
 
4.1%
Other values (11) 141138
28.3%

Situación Final frente al proceso
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
Culminado
28497 
Fuera del Proceso
20002 
Ausente del proceso
2850 
No ha ingresado
 
2647
En Proceso
 
2311

Length

Max length19
Median length9
Mean length12.671107
Min length9

Characters and Unicode

Total characters713472
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEn Proceso
2nd rowEn Proceso
3rd rowFuera del Proceso
4th rowEn Proceso
5th rowEn Proceso

Common Values

ValueCountFrequency (%)
Culminado 28497
50.6%
Fuera del Proceso 20002
35.5%
Ausente del proceso 2850
 
5.1%
No ha ingresado 2647
 
4.7%
En Proceso 2311
 
4.1%

Length

2025-02-08T12:31:59.655071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:31:59.767615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
culminado 28497
26.0%
proceso 25163
23.0%
del 22852
20.8%
fuera 20002
18.2%
ausente 2850
 
2.6%
no 2647
 
2.4%
ha 2647
 
2.4%
ingresado 2647
 
2.4%
en 2311
 
2.1%

Most occurring characters

ValueCountFrequency (%)
o 84117
11.8%
e 76364
10.7%
d 53996
 
7.6%
a 53793
 
7.5%
53309
 
7.5%
u 51349
 
7.2%
l 51349
 
7.2%
r 47812
 
6.7%
n 36305
 
5.1%
i 31144
 
4.4%
Other values (13) 173934
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 713472
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 84117
11.8%
e 76364
10.7%
d 53996
 
7.6%
a 53793
 
7.5%
53309
 
7.5%
u 51349
 
7.2%
l 51349
 
7.2%
r 47812
 
6.7%
n 36305
 
5.1%
i 31144
 
4.4%
Other values (13) 173934
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 713472
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 84117
11.8%
e 76364
10.7%
d 53996
 
7.6%
a 53793
 
7.5%
53309
 
7.5%
u 51349
 
7.2%
l 51349
 
7.2%
r 47812
 
6.7%
n 36305
 
5.1%
i 31144
 
4.4%
Other values (13) 173934
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 713472
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 84117
11.8%
e 76364
10.7%
d 53996
 
7.6%
a 53793
 
7.5%
53309
 
7.5%
u 51349
 
7.2%
l 51349
 
7.2%
r 47812
 
6.7%
n 36305
 
5.1%
i 31144
 
4.4%
Other values (13) 173934
24.4%

Departamento de residencia descripción
Categorical

High correlation 

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
Antioquia
12172 
Bogotá D.C.
5465 
Meta
3370 
Córdoba
3237 
Cesar
3019 
Other values (29)
29044 

Length

Max length56
Median length15
Mean length8.7456444
Min length4

Characters and Unicode

Total characters492441
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBogotá D.C.
2nd rowCesar
3rd rowValle del Cauca
4th rowMeta
5th rowCaquetá

Common Values

ValueCountFrequency (%)
Antioquia 12172
21.6%
Bogotá D.C. 5465
 
9.7%
Meta 3370
 
6.0%
Córdoba 3237
 
5.7%
Cesar 3019
 
5.4%
Valle del Cauca 2958
 
5.3%
<No Registra> 2601
 
4.6%
Santander 2420
 
4.3%
Magdalena 1996
 
3.5%
Cundinamarca 1832
 
3.3%
Other values (24) 17237
30.6%

Length

2025-02-08T12:31:59.955398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
antioquia 12172
16.6%
d.c 5465
 
7.4%
bogotá 5465
 
7.4%
cauca 3991
 
5.4%
santander 3780
 
5.1%
meta 3370
 
4.6%
córdoba 3237
 
4.4%
cesar 3019
 
4.1%
valle 2958
 
4.0%
del 2958
 
4.0%
Other values (35) 27013
36.8%

Most occurring characters

ValueCountFrequency (%)
a 71847
14.6%
o 37904
 
7.7%
i 36386
 
7.4%
t 33388
 
6.8%
n 28266
 
5.7%
e 26741
 
5.4%
u 24223
 
4.9%
r 21720
 
4.4%
C 21158
 
4.3%
l 18013
 
3.7%
Other values (36) 172795
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 492441
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 71847
14.6%
o 37904
 
7.7%
i 36386
 
7.4%
t 33388
 
6.8%
n 28266
 
5.7%
e 26741
 
5.4%
u 24223
 
4.9%
r 21720
 
4.4%
C 21158
 
4.3%
l 18013
 
3.7%
Other values (36) 172795
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 492441
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 71847
14.6%
o 37904
 
7.7%
i 36386
 
7.4%
t 33388
 
6.8%
n 28266
 
5.7%
e 26741
 
5.4%
u 24223
 
4.9%
r 21720
 
4.4%
C 21158
 
4.3%
l 18013
 
3.7%
Other values (36) 172795
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 492441
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 71847
14.6%
o 37904
 
7.7%
i 36386
 
7.4%
t 33388
 
6.8%
n 28266
 
5.7%
e 26741
 
5.4%
u 24223
 
4.9%
r 21720
 
4.4%
C 21158
 
4.3%
l 18013
 
3.7%
Other values (36) 172795
35.1%
Distinct886
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
2025-02-08T12:32:00.342031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length27
Median length23
Mean length8.8355622
Min length3

Characters and Unicode

Total characters497504
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99 ?
Unique (%)0.2%

Sample

1st rowBogotá
2nd rowLa Jagua De Ibirico
3rd rowPradera
4th rowVista Hermosa
5th rowCartagena Del Chairá
ValueCountFrequency (%)
bogotá 5465
 
7.5%
medellín 5288
 
7.2%
no 2601
 
3.6%
registra 2601
 
3.6%
san 2430
 
3.3%
de 1978
 
2.7%
puerto 1884
 
2.6%
villavicencio 1683
 
2.3%
cali 1656
 
2.3%
montería 1560
 
2.1%
Other values (879) 45937
62.9%
2025-02-08T12:32:00.869781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 68352
 
13.7%
e 40314
 
8.1%
o 37486
 
7.5%
l 33407
 
6.7%
r 31052
 
6.2%
n 27211
 
5.5%
i 26208
 
5.3%
t 22449
 
4.5%
16776
 
3.4%
u 15240
 
3.1%
Other values (50) 179009
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 497504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 68352
 
13.7%
e 40314
 
8.1%
o 37486
 
7.5%
l 33407
 
6.7%
r 31052
 
6.2%
n 27211
 
5.5%
i 26208
 
5.3%
t 22449
 
4.5%
16776
 
3.4%
u 15240
 
3.1%
Other values (50) 179009
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 497504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 68352
 
13.7%
e 40314
 
8.1%
o 37486
 
7.5%
l 33407
 
6.7%
r 31052
 
6.2%
n 27211
 
5.5%
i 26208
 
5.3%
t 22449
 
4.5%
16776
 
3.4%
u 15240
 
3.1%
Other values (50) 179009
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 497504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 68352
 
13.7%
e 40314
 
8.1%
o 37486
 
7.5%
l 33407
 
6.7%
r 31052
 
6.2%
n 27211
 
5.5%
i 26208
 
5.3%
t 22449
 
4.5%
16776
 
3.4%
u 15240
 
3.1%
Other values (50) 179009
36.0%

Departamento de residencia
Categorical

High correlation 

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
05
12172 
11
5465 
50
3370 
23
3237 
20
3019 
Other values (29)
29044 

Length

Max length13
Median length2
Mean length2.5081251
Min length2

Characters and Unicode

Total characters141225
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row11
2nd row20
3rd row76
4th row50
5th row18

Common Values

ValueCountFrequency (%)
05 12172
21.6%
11 5465
 
9.7%
50 3370
 
6.0%
23 3237
 
5.7%
20 3019
 
5.4%
76 2958
 
5.3%
<No Registra> 2601
 
4.6%
68 2420
 
4.3%
47 1996
 
3.5%
25 1832
 
3.3%
Other values (24) 17237
30.6%

Length

2025-02-08T12:32:01.021828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
05 12172
20.7%
11 5465
 
9.3%
50 3370
 
5.7%
23 3237
 
5.5%
20 3019
 
5.1%
76 2958
 
5.0%
no 2601
 
4.4%
registra 2601
 
4.4%
68 2420
 
4.1%
47 1996
 
3.4%
Other values (25) 19069
32.4%

Most occurring characters

ValueCountFrequency (%)
5 21494
15.2%
0 20833
14.8%
1 17673
12.5%
2 9595
 
6.8%
7 8736
 
6.2%
6 8584
 
6.1%
8 6885
 
4.9%
3 6441
 
4.6%
4 5557
 
3.9%
s 2601
 
1.8%
Other values (13) 32826
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 141225
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 21494
15.2%
0 20833
14.8%
1 17673
12.5%
2 9595
 
6.8%
7 8736
 
6.2%
6 8584
 
6.1%
8 6885
 
4.9%
3 6441
 
4.6%
4 5557
 
3.9%
s 2601
 
1.8%
Other values (13) 32826
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 141225
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 21494
15.2%
0 20833
14.8%
1 17673
12.5%
2 9595
 
6.8%
7 8736
 
6.2%
6 8584
 
6.1%
8 6885
 
4.9%
3 6441
 
4.6%
4 5557
 
3.9%
s 2601
 
1.8%
Other values (13) 32826
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 141225
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 21494
15.2%
0 20833
14.8%
1 17673
12.5%
2 9595
 
6.8%
7 8736
 
6.2%
6 8584
 
6.1%
8 6885
 
4.9%
3 6441
 
4.6%
4 5557
 
3.9%
s 2601
 
1.8%
Other values (13) 32826
23.2%
Distinct948
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
2025-02-08T12:32:01.446554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length5
Mean length5.3695455
Min length5

Characters and Unicode

Total characters302343
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique111 ?
Unique (%)0.2%

Sample

1st row11001
2nd row20400
3rd row76563
4th row50711
5th row18150
ValueCountFrequency (%)
11001 5465
 
9.3%
05001 5288
 
9.0%
no 2601
 
4.4%
registra 2601
 
4.4%
50001 1683
 
2.9%
76001 1656
 
2.8%
23001 1560
 
2.6%
20001 1494
 
2.5%
47001 1049
 
1.8%
18001 815
 
1.4%
Other values (939) 34696
58.9%
2025-02-08T12:32:02.038804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 85545
28.3%
1 51882
17.2%
5 32654
 
10.8%
7 17923
 
5.9%
8 16949
 
5.6%
6 15728
 
5.2%
2 15688
 
5.2%
3 13316
 
4.4%
4 12889
 
4.3%
9 5956
 
2.0%
Other values (13) 33813
 
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 302343
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 85545
28.3%
1 51882
17.2%
5 32654
 
10.8%
7 17923
 
5.9%
8 16949
 
5.6%
6 15728
 
5.2%
2 15688
 
5.2%
3 13316
 
4.4%
4 12889
 
4.3%
9 5956
 
2.0%
Other values (13) 33813
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 302343
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 85545
28.3%
1 51882
17.2%
5 32654
 
10.8%
7 17923
 
5.9%
8 16949
 
5.6%
6 15728
 
5.2%
2 15688
 
5.2%
3 13316
 
4.4%
4 12889
 
4.3%
9 5956
 
2.0%
Other values (13) 33813
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 302343
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 85545
28.3%
1 51882
17.2%
5 32654
 
10.8%
7 17923
 
5.9%
8 16949
 
5.6%
6 15728
 
5.2%
2 15688
 
5.2%
3 13316
 
4.4%
4 12889
 
4.3%
9 5956
 
2.0%
Other values (13) 33813
 
11.2%

BeneficioTRV
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
No
53506 
 
2801

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters112614
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd rowNo
4th row
5th row

Common Values

ValueCountFrequency (%)
No 53506
95.0%
2801
 
5.0%

Length

2025-02-08T12:32:02.186019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:32:02.272123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 53506
95.0%
2801
 
5.0%

Most occurring characters

ValueCountFrequency (%)
N 53506
47.5%
o 53506
47.5%
S 2801
 
2.5%
í 2801
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 53506
47.5%
o 53506
47.5%
S 2801
 
2.5%
í 2801
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 53506
47.5%
o 53506
47.5%
S 2801
 
2.5%
í 2801
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 53506
47.5%
o 53506
47.5%
S 2801
 
2.5%
í 2801
 
2.5%

BeneficioFA
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
No
55917 
 
390

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters112614
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 55917
99.3%
390
 
0.7%

Length

2025-02-08T12:32:02.380745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:32:02.471607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 55917
99.3%
390
 
0.7%

Most occurring characters

ValueCountFrequency (%)
N 55917
49.7%
o 55917
49.7%
S 390
 
0.3%
í 390
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 55917
49.7%
o 55917
49.7%
S 390
 
0.3%
í 390
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 55917
49.7%
o 55917
49.7%
S 390
 
0.3%
í 390
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 55917
49.7%
o 55917
49.7%
S 390
 
0.3%
í 390
 
0.3%

BeneficioFPT
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
No
56146 
 
161

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters112614
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 56146
99.7%
161
 
0.3%

Length

2025-02-08T12:32:02.578517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:32:02.667236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 56146
99.7%
161
 
0.3%

Most occurring characters

ValueCountFrequency (%)
N 56146
49.9%
o 56146
49.9%
S 161
 
0.1%
í 161
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 56146
49.9%
o 56146
49.9%
S 161
 
0.1%
í 161
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 56146
49.9%
o 56146
49.9%
S 161
 
0.1%
í 161
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 56146
49.9%
o 56146
49.9%
S 161
 
0.1%
í 161
 
0.1%

BeneficioPDT
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
No
53351 
 
2956

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters112614
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd rowNo
4th row
5th row

Common Values

ValueCountFrequency (%)
No 53351
94.8%
2956
 
5.2%

Length

2025-02-08T12:32:02.776887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:32:02.865336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 53351
94.8%
2956
 
5.2%

Most occurring characters

ValueCountFrequency (%)
N 53351
47.4%
o 53351
47.4%
S 2956
 
2.6%
í 2956
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 53351
47.4%
o 53351
47.4%
S 2956
 
2.6%
í 2956
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 53351
47.4%
o 53351
47.4%
S 2956
 
2.6%
í 2956
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 53351
47.4%
o 53351
47.4%
S 2956
 
2.6%
í 2956
 
2.6%

Nivel Educativo
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
Bachiller
18069 
Básica Primaria
14929 
Por Establecer
10897 
Básica Secundaria
8437 
Alfabetización
3975 

Length

Max length17
Median length15
Mean length13.110146
Min length9

Characters and Unicode

Total characters738193
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBachiller
2nd rowBásica Primaria
3rd rowBásica Primaria
4th rowBásica Primaria
5th rowPor Establecer

Common Values

ValueCountFrequency (%)
Bachiller 18069
32.1%
Básica Primaria 14929
26.5%
Por Establecer 10897
19.4%
Básica Secundaria 8437
15.0%
Alfabetización 3975
 
7.1%

Length

2025-02-08T12:32:03.006642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:32:03.130783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
básica 23366
25.8%
bachiller 18069
20.0%
primaria 14929
16.5%
por 10897
12.0%
establecer 10897
12.0%
secundaria 8437
 
9.3%
alfabetización 3975
 
4.4%

Most occurring characters

ValueCountFrequency (%)
a 107014
14.5%
i 87680
11.9%
r 78158
10.6%
c 64744
8.8%
e 52275
 
7.1%
l 51010
 
6.9%
B 41435
 
5.6%
s 34263
 
4.6%
34263
 
4.6%
P 25826
 
3.5%
Other values (15) 161525
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 738193
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 107014
14.5%
i 87680
11.9%
r 78158
10.6%
c 64744
8.8%
e 52275
 
7.1%
l 51010
 
6.9%
B 41435
 
5.6%
s 34263
 
4.6%
34263
 
4.6%
P 25826
 
3.5%
Other values (15) 161525
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 738193
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 107014
14.5%
i 87680
11.9%
r 78158
10.6%
c 64744
8.8%
e 52275
 
7.1%
l 51010
 
6.9%
B 41435
 
5.6%
s 34263
 
4.6%
34263
 
4.6%
P 25826
 
3.5%
Other values (15) 161525
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 738193
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 107014
14.5%
i 87680
11.9%
r 78158
10.6%
c 64744
8.8%
e 52275
 
7.1%
l 51010
 
6.9%
B 41435
 
5.6%
s 34263
 
4.6%
34263
 
4.6%
P 25826
 
3.5%
Other values (15) 161525
21.9%
Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
<No Aplica>
26740 
Técnico
9669 
Complementario
9080 
Semicalificado
6872 
Tecnológico
 
2417
Other values (9)
 
1529

Length

Max length44
Median length11
Mean length11.136058
Min length7

Characters and Unicode

Total characters627038
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<No Aplica>
2nd row<No Aplica>
3rd row<No Aplica>
4th row<No Aplica>
5th rowOperario

Common Values

ValueCountFrequency (%)
<No Aplica> 26740
47.5%
Técnico 9669
 
17.2%
Complementario 9080
 
16.1%
Semicalificado 6872
 
12.2%
Tecnológico 2417
 
4.3%
Operario 636
 
1.1%
Transversal 578
 
1.0%
Auxiliar 212
 
0.4%
Técnico Profesional 81
 
0.1%
Técnico Laboral 8
 
< 0.1%
Other values (4) 14
 
< 0.1%

Length

2025-02-08T12:32:03.301419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 26740
32.1%
aplica 26740
32.1%
técnico 9760
 
11.7%
complementario 9080
 
10.9%
semicalificado 6872
 
8.3%
tecnológico 2417
 
2.9%
operario 636
 
0.8%
transversal 578
 
0.7%
auxiliar 212
 
0.3%
profesional 81
 
0.1%
Other values (9) 59
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i 69811
 
11.1%
o 67195
 
10.7%
c 64888
 
10.3%
a 51714
 
8.2%
l 46005
 
7.3%
p 36479
 
5.8%
e 28784
 
4.6%
A 26952
 
4.3%
26868
 
4.3%
N 26740
 
4.3%
Other values (24) 181602
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 627038
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 69811
 
11.1%
o 67195
 
10.7%
c 64888
 
10.3%
a 51714
 
8.2%
l 46005
 
7.3%
p 36479
 
5.8%
e 28784
 
4.6%
A 26952
 
4.3%
26868
 
4.3%
N 26740
 
4.3%
Other values (24) 181602
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 627038
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 69811
 
11.1%
o 67195
 
10.7%
c 64888
 
10.3%
a 51714
 
8.2%
l 46005
 
7.3%
p 36479
 
5.8%
e 28784
 
4.6%
A 26952
 
4.3%
26868
 
4.3%
N 26740
 
4.3%
Other values (24) 181602
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 627038
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 69811
 
11.1%
o 67195
 
10.7%
c 64888
 
10.3%
a 51714
 
8.2%
l 46005
 
7.3%
p 36479
 
5.8%
e 28784
 
4.6%
A 26952
 
4.3%
26868
 
4.3%
N 26740
 
4.3%
Other values (24) 181602
29.0%
Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
<No Aplica>
26740 
AGROPECUARIA
4768 
FINANZAS Y ADMINISTRACION
3320 
<No Registra>
2840 
MERCADEO Y VENTAS
 
2595
Other values (19)
16044 

Length

Max length43
Median length35
Mean length13.497736
Min length5

Characters and Unicode

Total characters760017
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<No Aplica>
2nd row<No Aplica>
3rd row<No Aplica>
4th row<No Aplica>
5th rowAGROPECUARIA

Common Values

ValueCountFrequency (%)
<No Aplica> 26740
47.5%
AGROPECUARIA 4768
 
8.5%
FINANZAS Y ADMINISTRACION 3320
 
5.9%
<No Registra> 2840
 
5.0%
MERCADEO Y VENTAS 2595
 
4.6%
ALIMENTOS Y BEBIDAS 1995
 
3.5%
SISTEMAS 1959
 
3.5%
OTROS 1850
 
3.3%
MECANICA AUTOMOTRIZ Y DE MOTOS 1817
 
3.2%
SERVICIOS 1304
 
2.3%
Other values (14) 7119
 
12.6%

Length

2025-02-08T12:32:03.479424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 29580
25.6%
aplica 26740
23.2%
y 11584
 
10.0%
agropecuaria 4768
 
4.1%
finanzas 3320
 
2.9%
administracion 3320
 
2.9%
registra 2840
 
2.5%
mercadeo 2595
 
2.2%
ventas 2595
 
2.2%
mecanica 2562
 
2.2%
Other values (34) 25440
22.1%

Most occurring characters

ValueCountFrequency (%)
A 84560
 
11.1%
59037
 
7.8%
N 58275
 
7.7%
I 41412
 
5.4%
E 33034
 
4.3%
O 32449
 
4.3%
R 32318
 
4.3%
S 31664
 
4.2%
> 29580
 
3.9%
< 29580
 
3.9%
Other values (29) 328108
43.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 760017
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 84560
 
11.1%
59037
 
7.8%
N 58275
 
7.7%
I 41412
 
5.4%
E 33034
 
4.3%
O 32449
 
4.3%
R 32318
 
4.3%
S 31664
 
4.2%
> 29580
 
3.9%
< 29580
 
3.9%
Other values (29) 328108
43.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 760017
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 84560
 
11.1%
59037
 
7.8%
N 58275
 
7.7%
I 41412
 
5.4%
E 33034
 
4.3%
O 32449
 
4.3%
R 32318
 
4.3%
S 31664
 
4.2%
> 29580
 
3.9%
< 29580
 
3.9%
Other values (29) 328108
43.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 760017
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 84560
 
11.1%
59037
 
7.8%
N 58275
 
7.7%
I 41412
 
5.4%
E 33034
 
4.3%
O 32449
 
4.3%
R 32318
 
4.3%
S 31664
 
4.2%
> 29580
 
3.9%
< 29580
 
3.9%
Other values (29) 328108
43.2%

OcupacionEconomica
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
Ocupados en el sector Informal
27487 
<No Registra>
11639 
No Aplica
7397 
Población Económicamente Inactiva
5210 
Desocupados
4574 

Length

Max length33
Median length30
Mean length22.461399
Min length9

Characters and Unicode

Total characters1264734
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPoblación Económicamente Inactiva
2nd rowPoblación Económicamente Inactiva
3rd rowPoblación Económicamente Inactiva
4th rowPoblación Económicamente Inactiva
5th rowPoblación Económicamente Inactiva

Common Values

ValueCountFrequency (%)
Ocupados en el sector Informal 27487
48.8%
<No Registra> 11639
20.7%
No Aplica 7397
 
13.1%
Población Económicamente Inactiva 5210
 
9.3%
Desocupados 4574
 
8.1%

Length

2025-02-08T12:32:03.642173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:32:03.758169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ocupados 27487
14.0%
en 27487
14.0%
el 27487
14.0%
sector 27487
14.0%
informal 27487
14.0%
no 19036
9.7%
registra 11639
5.9%
aplica 7397
 
3.8%
población 5210
 
2.7%
económicamente 5210
 
2.7%
Other values (2) 9784
 
5.0%

Most occurring characters

ValueCountFrequency (%)
139404
 
11.0%
o 121065
 
9.6%
e 109094
 
8.6%
a 99424
 
7.9%
c 87785
 
6.9%
n 75814
 
6.0%
s 75761
 
6.0%
l 67581
 
5.3%
r 66613
 
5.3%
t 49546
 
3.9%
Other values (20) 372647
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1264734
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
139404
 
11.0%
o 121065
 
9.6%
e 109094
 
8.6%
a 99424
 
7.9%
c 87785
 
6.9%
n 75814
 
6.0%
s 75761
 
6.0%
l 67581
 
5.3%
r 66613
 
5.3%
t 49546
 
3.9%
Other values (20) 372647
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1264734
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
139404
 
11.0%
o 121065
 
9.6%
e 109094
 
8.6%
a 99424
 
7.9%
c 87785
 
6.9%
n 75814
 
6.0%
s 75761
 
6.0%
l 67581
 
5.3%
r 66613
 
5.3%
t 49546
 
3.9%
Other values (20) 372647
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1264734
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
139404
 
11.0%
o 121065
 
9.6%
e 109094
 
8.6%
a 99424
 
7.9%
c 87785
 
6.9%
n 75814
 
6.0%
s 75761
 
6.0%
l 67581
 
5.3%
r 66613
 
5.3%
t 49546
 
3.9%
Other values (20) 372647
29.5%

Desembolso BIE
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
No
28589 
27718 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters112614
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 28589
50.8%
27718
49.2%

Length

2025-02-08T12:32:03.929968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:32:04.058344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 28589
50.8%
27718
49.2%

Most occurring characters

ValueCountFrequency (%)
N 28589
25.4%
o 28589
25.4%
S 27718
24.6%
í 27718
24.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 28589
25.4%
o 28589
25.4%
S 27718
24.6%
í 27718
24.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 28589
25.4%
o 28589
25.4%
S 27718
24.6%
í 27718
24.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 28589
25.4%
o 28589
25.4%
S 27718
24.6%
í 27718
24.6%

Tipo de BIE Accedido
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
<No Aplica>
28589 
Plan de Negocio
26924 
Vivienda
 
756
Educación Superior
 
38

Length

Max length18
Median length11
Mean length12.877102
Min length8

Characters and Unicode

Total characters725071
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<No Aplica>
2nd row<No Aplica>
3rd row<No Aplica>
4th row<No Aplica>
5th row<No Aplica>

Common Values

ValueCountFrequency (%)
<No Aplica> 28589
50.8%
Plan de Negocio 26924
47.8%
Vivienda 756
 
1.3%
Educación Superior 38
 
0.1%

Length

2025-02-08T12:32:04.200669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:32:04.359763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 28589
20.6%
aplica 28589
20.6%
plan 26924
19.4%
de 26924
19.4%
negocio 26924
19.4%
vivienda 756
 
0.5%
educación 38
 
< 0.1%
superior 38
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 82475
11.4%
82475
11.4%
i 57101
 
7.9%
a 56307
 
7.8%
c 55589
 
7.7%
l 55513
 
7.7%
N 55513
 
7.7%
e 54642
 
7.5%
p 28627
 
3.9%
< 28589
 
3.9%
Other values (13) 168240
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 725071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 82475
11.4%
82475
11.4%
i 57101
 
7.9%
a 56307
 
7.8%
c 55589
 
7.7%
l 55513
 
7.7%
N 55513
 
7.7%
e 54642
 
7.5%
p 28627
 
3.9%
< 28589
 
3.9%
Other values (13) 168240
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 725071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 82475
11.4%
82475
11.4%
i 57101
 
7.9%
a 56307
 
7.8%
c 55589
 
7.7%
l 55513
 
7.7%
N 55513
 
7.7%
e 54642
 
7.5%
p 28627
 
3.9%
< 28589
 
3.9%
Other values (13) 168240
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 725071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 82475
11.4%
82475
11.4%
i 57101
 
7.9%
a 56307
 
7.8%
c 55589
 
7.7%
l 55513
 
7.7%
N 55513
 
7.7%
e 54642
 
7.5%
p 28627
 
3.9%
< 28589
 
3.9%
Other values (13) 168240
23.2%

DesagregadoDesembolsoBIE
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
Posee desembolso BIE
27718 
No está en Proceso
21260 
Culminado con agotamiento de tiempo para acceder a BIE
5466 
No posee desembolso BIE
 
1757
Culminado sin agotamiento de tiempo para acceder a BIE
 
106

Length

Max length54
Median length23
Mean length22.703021
Min length18

Characters and Unicode

Total characters1278339
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo posee desembolso BIE
2nd rowNo posee desembolso BIE
3rd rowNo está en Proceso
4th rowNo posee desembolso BIE
5th rowNo posee desembolso BIE

Common Values

ValueCountFrequency (%)
Posee desembolso BIE 27718
49.2%
No está en Proceso 21260
37.8%
Culminado con agotamiento de tiempo para acceder a BIE 5466
 
9.7%
No posee desembolso BIE 1757
 
3.1%
Culminado sin agotamiento de tiempo para acceder a BIE 106
 
0.2%

Length

2025-02-08T12:32:04.602963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:32:04.780643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bie 35047
15.6%
posee 29475
13.1%
desembolso 29475
13.1%
no 23017
10.2%
está 21260
9.4%
en 21260
9.4%
proceso 21260
9.4%
culminado 5572
 
2.5%
agotamiento 5572
 
2.5%
de 5572
 
2.5%
Other values (6) 27860
12.4%

Most occurring characters

ValueCountFrequency (%)
e 209540
16.4%
o 181716
14.2%
169063
13.2%
s 131051
 
10.3%
P 48978
 
3.8%
d 46191
 
3.6%
m 46191
 
3.6%
a 39004
 
3.1%
n 37976
 
3.0%
t 37976
 
3.0%
Other values (14) 330653
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1278339
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 209540
16.4%
o 181716
14.2%
169063
13.2%
s 131051
 
10.3%
P 48978
 
3.8%
d 46191
 
3.6%
m 46191
 
3.6%
a 39004
 
3.1%
n 37976
 
3.0%
t 37976
 
3.0%
Other values (14) 330653
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1278339
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 209540
16.4%
o 181716
14.2%
169063
13.2%
s 131051
 
10.3%
P 48978
 
3.8%
d 46191
 
3.6%
m 46191
 
3.6%
a 39004
 
3.1%
n 37976
 
3.0%
t 37976
 
3.0%
Other values (14) 330653
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1278339
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 209540
16.4%
o 181716
14.2%
169063
13.2%
s 131051
 
10.3%
P 48978
 
3.8%
d 46191
 
3.6%
m 46191
 
3.6%
a 39004
 
3.1%
n 37976
 
3.0%
t 37976
 
3.0%
Other values (14) 330653
25.9%

Estado ISUN
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
<No Aplica>
28590 
En Funcionamiento
11259 
Cerrado
9994 
Pendiente por visita ISUN
6464 

Length

Max length25
Median length11
Mean length13.096968
Min length7

Characters and Unicode

Total characters737451
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<No Aplica>
2nd row<No Aplica>
3rd row<No Aplica>
4th row<No Aplica>
5th row<No Aplica>

Common Values

ValueCountFrequency (%)
<No Aplica> 28590
50.8%
En Funcionamiento 11259
 
20.0%
Cerrado 9994
 
17.7%
Pendiente por visita ISUN 6464
 
11.5%

Length

2025-02-08T12:32:05.331142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:32:05.520313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 28590
24.7%
aplica 28590
24.7%
en 11259
 
9.7%
funcionamiento 11259
 
9.7%
cerrado 9994
 
8.6%
pendiente 6464
 
5.6%
por 6464
 
5.6%
visita 6464
 
5.6%
isun 6464
 
5.6%

Most occurring characters

ValueCountFrequency (%)
i 70500
 
9.6%
o 67566
 
9.2%
59241
 
8.0%
n 57964
 
7.9%
a 56307
 
7.6%
e 40645
 
5.5%
c 39849
 
5.4%
p 35054
 
4.8%
N 35054
 
4.8%
< 28590
 
3.9%
Other values (17) 246681
33.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 737451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 70500
 
9.6%
o 67566
 
9.2%
59241
 
8.0%
n 57964
 
7.9%
a 56307
 
7.6%
e 40645
 
5.5%
c 39849
 
5.4%
p 35054
 
4.8%
N 35054
 
4.8%
< 28590
 
3.9%
Other values (17) 246681
33.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 737451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 70500
 
9.6%
o 67566
 
9.2%
59241
 
8.0%
n 57964
 
7.9%
a 56307
 
7.6%
e 40645
 
5.5%
c 39849
 
5.4%
p 35054
 
4.8%
N 35054
 
4.8%
< 28590
 
3.9%
Other values (17) 246681
33.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 737451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 70500
 
9.6%
o 67566
 
9.2%
59241
 
8.0%
n 57964
 
7.9%
a 56307
 
7.6%
e 40645
 
5.5%
c 39849
 
5.4%
p 35054
 
4.8%
N 35054
 
4.8%
< 28590
 
3.9%
Other values (17) 246681
33.5%

Posee Servicio Social?
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
Posee Certificación de Servicio Social
32254 
No está vinculado a Servicio Social
23641 
Se encuentra vinculado a Servicio Social
 
412

Length

Max length40
Median length38
Mean length36.755057
Min length35

Characters and Unicode

Total characters2069567
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSe encuentra vinculado a Servicio Social
2nd rowPosee Certificación de Servicio Social
3rd rowPosee Certificación de Servicio Social
4th rowPosee Certificación de Servicio Social
5th rowNo está vinculado a Servicio Social

Common Values

ValueCountFrequency (%)
Posee Certificación de Servicio Social 32254
57.3%
No está vinculado a Servicio Social 23641
42.0%
Se encuentra vinculado a Servicio Social 412
 
0.7%

Length

2025-02-08T12:32:05.839068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:32:05.976758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
servicio 56307
18.4%
social 56307
18.4%
posee 32254
10.6%
certificación 32254
10.6%
de 32254
10.6%
vinculado 24053
7.9%
a 24053
7.9%
no 23641
7.7%
está 23641
7.7%
se 412
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i 289736
14.0%
249281
12.0%
e 210200
10.2%
c 201587
9.7%
o 192562
9.3%
a 137079
 
6.6%
S 113026
 
5.5%
r 88973
 
4.3%
l 80360
 
3.9%
v 80360
 
3.9%
Other values (11) 426403
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2069567
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 289736
14.0%
249281
12.0%
e 210200
10.2%
c 201587
9.7%
o 192562
9.3%
a 137079
 
6.6%
S 113026
 
5.5%
r 88973
 
4.3%
l 80360
 
3.9%
v 80360
 
3.9%
Other values (11) 426403
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2069567
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 289736
14.0%
249281
12.0%
e 210200
10.2%
c 201587
9.7%
o 192562
9.3%
a 137079
 
6.6%
S 113026
 
5.5%
r 88973
 
4.3%
l 80360
 
3.9%
v 80360
 
3.9%
Other values (11) 426403
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2069567
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 289736
14.0%
249281
12.0%
e 210200
10.2%
c 201587
9.7%
o 192562
9.3%
a 137079
 
6.6%
S 113026
 
5.5%
r 88973
 
4.3%
l 80360
 
3.9%
v 80360
 
3.9%
Other values (11) 426403
20.6%

Estado de la vinculación ASS
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
Certificado
32254 
<No Aplica>
23069 
Vinculado
 
412
Abandono sin justa causa
 
283
Abandono con justa causa
 
253

Length

Max length73
Median length11
Mean length11.148756
Min length9

Characters and Unicode

Total characters627753
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVinculado
2nd rowCertificado
3rd rowCertificado
4th rowCertificado
5th row<No Aplica>

Common Values

ValueCountFrequency (%)
Certificado 32254
57.3%
<No Aplica> 23069
41.0%
Vinculado 412
 
0.7%
Abandono sin justa causa 283
 
0.5%
Abandono con justa causa 253
 
0.4%
No vinculado por limitaciones físicas o mentales permanentes certificadas 36
 
0.1%

Length

2025-02-08T12:32:06.242831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:32:06.485769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
certificado 32254
39.7%
no 23105
28.4%
aplica 23069
28.4%
abandono 536
 
0.7%
justa 536
 
0.7%
causa 536
 
0.7%
vinculado 448
 
0.6%
sin 283
 
0.3%
con 253
 
0.3%
por 36
 
< 0.1%
Other values (6) 216
 
0.3%

Most occurring characters

ValueCountFrequency (%)
i 88524
14.1%
a 58131
 
9.3%
o 57240
 
9.1%
c 56704
 
9.0%
d 33274
 
5.3%
t 32934
 
5.2%
e 32506
 
5.2%
r 32362
 
5.2%
f 32326
 
5.1%
C 32254
 
5.1%
Other values (16) 171498
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 627753
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 88524
14.1%
a 58131
 
9.3%
o 57240
 
9.1%
c 56704
 
9.0%
d 33274
 
5.3%
t 32934
 
5.2%
e 32506
 
5.2%
r 32362
 
5.2%
f 32326
 
5.1%
C 32254
 
5.1%
Other values (16) 171498
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 627753
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 88524
14.1%
a 58131
 
9.3%
o 57240
 
9.1%
c 56704
 
9.0%
d 33274
 
5.3%
t 32934
 
5.2%
e 32506
 
5.2%
r 32362
 
5.2%
f 32326
 
5.1%
C 32254
 
5.1%
Other values (16) 171498
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 627753
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 88524
14.1%
a 58131
 
9.3%
o 57240
 
9.1%
c 56704
 
9.0%
d 33274
 
5.3%
t 32934
 
5.2%
e 32506
 
5.2%
r 32362
 
5.2%
f 32326
 
5.1%
C 32254
 
5.1%
Other values (16) 171498
27.3%

Tipo de ASS Vinculada
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
<No Aplica>
23069 
Embellecimiento de Espacio Publico
16893 
Recuperación Ambiental
6078 
Generación de espacios de recreación, Arte, Cultura y Deporte
4112 
Aporte de habilidades Especiales que le participante ponga a disposición de la comunidad
3198 
Other values (2)
2957 

Length

Max length88
Median length86
Mean length29.935763
Min length11

Characters and Unicode

Total characters1685593
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMultiplicadores del Conocimiento
2nd rowRecuperación Ambiental
3rd rowEmbellecimiento de Espacio Publico
4th rowEmbellecimiento de Espacio Publico
5th row<No Aplica>

Common Values

ValueCountFrequency (%)
<No Aplica> 23069
41.0%
Embellecimiento de Espacio Publico 16893
30.0%
Recuperación Ambiental 6078
 
10.8%
Generación de espacios de recreación, Arte, Cultura y Deporte 4112
 
7.3%
Aporte de habilidades Especiales que le participante ponga a disposición de la comunidad 3198
 
5.7%
Acompañamiento a la atención en Salud y atención Alimentaria a comunidades vulnerables 1794
 
3.2%
Multiplicadores del Conocimiento 1163
 
2.1%

Length

2025-02-08T12:32:06.797697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:32:07.041862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
de 31513
13.7%
no 23069
 
10.1%
aplica 23069
 
10.1%
embellecimiento 16893
 
7.4%
espacio 16893
 
7.4%
publico 16893
 
7.4%
a 6786
 
3.0%
ambiental 6078
 
2.6%
recuperación 6078
 
2.6%
y 5906
 
2.6%
Other values (26) 76287
33.2%

Most occurring characters

ValueCountFrequency (%)
e 173549
 
10.3%
173158
 
10.3%
i 159331
 
9.5%
c 124646
 
7.4%
a 120039
 
7.1%
l 109189
 
6.5%
o 104798
 
6.2%
p 76409
 
4.5%
n 72649
 
4.3%
d 55211
 
3.3%
Other values (26) 516614
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1685593
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 173549
 
10.3%
173158
 
10.3%
i 159331
 
9.5%
c 124646
 
7.4%
a 120039
 
7.1%
l 109189
 
6.5%
o 104798
 
6.2%
p 76409
 
4.5%
n 72649
 
4.3%
d 55211
 
3.3%
Other values (26) 516614
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1685593
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 173549
 
10.3%
173158
 
10.3%
i 159331
 
9.5%
c 124646
 
7.4%
a 120039
 
7.1%
l 109189
 
6.5%
o 104798
 
6.2%
p 76409
 
4.5%
n 72649
 
4.3%
d 55211
 
3.3%
Other values (26) 516614
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1685593
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 173549
 
10.3%
173158
 
10.3%
i 159331
 
9.5%
c 124646
 
7.4%
a 120039
 
7.1%
l 109189
 
6.5%
o 104798
 
6.2%
p 76409
 
4.5%
n 72649
 
4.3%
d 55211
 
3.3%
Other values (26) 516614
30.6%

Posee Censo de Familia?
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
35255 
No
21052 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters112614
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
35255
62.6%
No 21052
37.4%

Length

2025-02-08T12:32:07.400204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:32:07.561209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
35255
62.6%
no 21052
37.4%

Most occurring characters

ValueCountFrequency (%)
S 35255
31.3%
í 35255
31.3%
N 21052
18.7%
o 21052
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 35255
31.3%
í 35255
31.3%
N 21052
18.7%
o 21052
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 35255
31.3%
í 35255
31.3%
N 21052
18.7%
o 21052
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 35255
31.3%
í 35255
31.3%
N 21052
18.7%
o 21052
18.7%

Posee Cónyuge o Compañero(a)?
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
<No Aplica>
21052 
17640 
No
17549 
<No Registra>
 
66

Length

Max length13
Median length2
Mean length5.3778038
Min length2

Characters and Unicode

Total characters302808
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd row
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
<No Aplica> 21052
37.4%
17640
31.3%
No 17549
31.2%
<No Registra> 66
 
0.1%

Length

2025-02-08T12:32:07.775327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:32:08.007509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 38667
49.9%
aplica 21052
27.2%
17640
22.8%
registra 66
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 38667
12.8%
N 38667
12.8%
< 21118
 
7.0%
i 21118
 
7.0%
> 21118
 
7.0%
a 21118
 
7.0%
21118
 
7.0%
l 21052
 
7.0%
p 21052
 
7.0%
c 21052
 
7.0%
Other values (9) 56728
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 302808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 38667
12.8%
N 38667
12.8%
< 21118
 
7.0%
i 21118
 
7.0%
> 21118
 
7.0%
a 21118
 
7.0%
21118
 
7.0%
l 21052
 
7.0%
p 21052
 
7.0%
c 21052
 
7.0%
Other values (9) 56728
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 302808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 38667
12.8%
N 38667
12.8%
< 21118
 
7.0%
i 21118
 
7.0%
> 21118
 
7.0%
a 21118
 
7.0%
21118
 
7.0%
l 21052
 
7.0%
p 21052
 
7.0%
c 21052
 
7.0%
Other values (9) 56728
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 302808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 38667
12.8%
N 38667
12.8%
< 21118
 
7.0%
i 21118
 
7.0%
> 21118
 
7.0%
a 21118
 
7.0%
21118
 
7.0%
l 21052
 
7.0%
p 21052
 
7.0%
c 21052
 
7.0%
Other values (9) 56728
18.7%

N° de Hijos
Real number (ℝ)

High correlation  Zeros 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1165752
Minimum-2
Maximum11
Zeros17296
Zeros (%)30.7%
Negative21118
Negative (%)37.5%
Memory size440.0 KiB
2025-02-08T12:32:08.245561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-2
median0
Q31
95-th percentile3
Maximum11
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7675286
Coefficient of variation (CV)-15.162132
Kurtosis-0.02560692
Mean-0.1165752
Median Absolute Deviation (MAD)2
Skewness0.63187821
Sum-6564
Variance3.1241573
MonotonicityNot monotonic
2025-02-08T12:32:08.505765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
-2 21052
37.4%
0 17296
30.7%
1 7202
 
12.8%
2 6108
 
10.8%
3 2954
 
5.2%
4 1086
 
1.9%
5 364
 
0.6%
6 124
 
0.2%
-1 66
 
0.1%
7 37
 
0.1%
Other values (4) 18
 
< 0.1%
ValueCountFrequency (%)
-2 21052
37.4%
-1 66
 
0.1%
0 17296
30.7%
1 7202
 
12.8%
2 6108
 
10.8%
3 2954
 
5.2%
4 1086
 
1.9%
5 364
 
0.6%
6 124
 
0.2%
7 37
 
0.1%
ValueCountFrequency (%)
11 3
 
< 0.1%
10 1
 
< 0.1%
9 4
 
< 0.1%
8 10
 
< 0.1%
7 37
 
0.1%
6 124
 
0.2%
5 364
 
0.6%
4 1086
 
1.9%
3 2954
5.2%
2 6108
10.8%

Total Integrantes grupo familiar
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3502406
Minimum-1
Maximum28
Zeros0
Zeros (%)0.0%
Negative21058
Negative (%)37.4%
Memory size440.0 KiB
2025-02-08T12:32:08.773304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median1
Q33
95-th percentile5
Maximum28
Range29
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2281507
Coefficient of variation (CV)1.6501879
Kurtosis-0.043278191
Mean1.3502406
Median Absolute Deviation (MAD)2
Skewness0.59120504
Sum76028
Variance4.9646557
MonotonicityNot monotonic
2025-02-08T12:32:09.074664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
-1 21058
37.4%
1 10507
18.7%
3 6907
 
12.3%
2 6872
 
12.2%
4 5962
 
10.6%
5 2994
 
5.3%
6 1251
 
2.2%
7 476
 
0.8%
8 173
 
0.3%
9 57
 
0.1%
Other values (8) 50
 
0.1%
ValueCountFrequency (%)
-1 21058
37.4%
1 10507
18.7%
2 6872
 
12.2%
3 6907
 
12.3%
4 5962
 
10.6%
5 2994
 
5.3%
6 1251
 
2.2%
7 476
 
0.8%
8 173
 
0.3%
9 57
 
0.1%
ValueCountFrequency (%)
28 1
 
< 0.1%
16 1
 
< 0.1%
15 2
 
< 0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 4
 
< 0.1%
11 9
 
< 0.1%
10 30
 
0.1%
9 57
 
0.1%
8 173
0.3%

Posee Censo de Habitabilidad?
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
34424 
No
21883 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters112614
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
34424
61.1%
No 21883
38.9%

Length

2025-02-08T12:32:09.426199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:32:09.527529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
34424
61.1%
no 21883
38.9%

Most occurring characters

ValueCountFrequency (%)
S 34424
30.6%
í 34424
30.6%
N 21883
19.4%
o 21883
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 34424
30.6%
í 34424
30.6%
N 21883
19.4%
o 21883
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 34424
30.6%
í 34424
30.6%
N 21883
19.4%
o 21883
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112614
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 34424
30.6%
í 34424
30.6%
N 21883
19.4%
o 21883
19.4%

Tipo de Vivienda
Categorical

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
<No Aplica>
21883 
Casa
21743 
Apartamento
6345 
Habitación
 
1406
Cuarto(s)
 
1264
Other values (7)
3666 

Length

Max length102
Median length11
Mean length8.5649919
Min length4

Characters and Unicode

Total characters482269
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApartamento
2nd rowCasa
3rd rowCasa-Lote
4th rowCasa
5th rowCuarto(s)

Common Values

ValueCountFrequency (%)
<No Aplica> 21883
38.9%
Casa 21743
38.6%
Apartamento 6345
 
11.3%
Habitación 1406
 
2.5%
Cuarto(s) 1264
 
2.2%
Finca 1222
 
2.2%
Rancho 958
 
1.7%
Casa-Lote 797
 
1.4%
Otro tipo de vivienda (carpa, tienda, vagón, embarcación, cueva, refugio natural, puente, calle, etc.) 336
 
0.6%
Vivienda (casa) indígena 231
 
0.4%
Other values (2) 122
 
0.2%

Length

2025-02-08T12:32:09.650843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
casa 21974
26.5%
no 21885
26.4%
aplica 21883
26.4%
apartamento 6345
 
7.6%
habitación 1406
 
1.7%
cuarto(s 1264
 
1.5%
finca 1222
 
1.5%
rancho 958
 
1.2%
casa-lote 797
 
1.0%
vivienda 567
 
0.7%
Other values (15) 4721
 
5.7%

Most occurring characters

ValueCountFrequency (%)
a 90531
18.8%
o 32377
 
6.7%
p 29236
 
6.1%
i 28628
 
5.9%
A 28228
 
5.9%
c 27716
 
5.7%
26715
 
5.5%
s 24037
 
5.0%
C 23804
 
4.9%
l 22891
 
4.7%
Other values (28) 148106
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 482269
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 90531
18.8%
o 32377
 
6.7%
p 29236
 
6.1%
i 28628
 
5.9%
A 28228
 
5.9%
c 27716
 
5.7%
26715
 
5.5%
s 24037
 
5.0%
C 23804
 
4.9%
l 22891
 
4.7%
Other values (28) 148106
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 482269
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 90531
18.8%
o 32377
 
6.7%
p 29236
 
6.1%
i 28628
 
5.9%
A 28228
 
5.9%
c 27716
 
5.7%
26715
 
5.5%
s 24037
 
5.0%
C 23804
 
4.9%
l 22891
 
4.7%
Other values (28) 148106
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 482269
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 90531
18.8%
o 32377
 
6.7%
p 29236
 
6.1%
i 28628
 
5.9%
A 28228
 
5.9%
c 27716
 
5.7%
26715
 
5.5%
s 24037
 
5.0%
C 23804
 
4.9%
l 22891
 
4.7%
Other values (28) 148106
30.7%

Régimen de tenencia Vivienda
Categorical

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
<No Aplica>
21883 
En arriendo o subarriendo
16686 
Propia, totalmente pagada
7321 
Con permiso del propietario, sin pago alguno
4967 
Otra forma de tenencia (posesión sin título, ocupante de hecho, propiedad colectiva, etc)
 
1746
Other values (6)
3704 

Length

Max length90
Median length61
Mean length23.551885
Min length8

Characters and Unicode

Total characters1326136
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPropia, la están pagando
2nd rowEn arriendo o subarriendo
3rd rowPropia, totalmente pagada
4th rowCon permiso del propietario, sin pago alguno
5th rowCon permiso del propietario, sin pago alguno

Common Values

ValueCountFrequency (%)
<No Aplica> 21883
38.9%
En arriendo o subarriendo 16686
29.6%
Propia, totalmente pagada 7321
 
13.0%
Con permiso del propietario, sin pago alguno 4967
 
8.8%
Otra forma de tenencia (posesión sin título, ocupante de hecho, propiedad colectiva, etc) 1746
 
3.1%
Es usufructo 1377
 
2.4%
Posesión sin título (ocupante de hecho) o propiedad colectiva 1155
 
2.1%
Propia, la están pagando 733
 
1.3%
Familiar 346
 
0.6%
Sana posesión con título 92
 
0.2%

Length

2025-02-08T12:32:09.800107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 21884
 
10.6%
aplica 21883
 
10.6%
o 17841
 
8.6%
en 16686
 
8.1%
arriendo 16686
 
8.1%
subarriendo 16686
 
8.1%
propia 8054
 
3.9%
sin 7868
 
3.8%
totalmente 7321
 
3.5%
pagada 7321
 
3.5%
Other values (25) 64507
31.2%

Most occurring characters

ValueCountFrequency (%)
152176
11.5%
o 139812
 
10.5%
a 124511
 
9.4%
r 97816
 
7.4%
i 97312
 
7.3%
e 88131
 
6.6%
n 86217
 
6.5%
p 68400
 
5.2%
d 56842
 
4.3%
l 46111
 
3.5%
Other values (27) 368808
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1326136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
152176
11.5%
o 139812
 
10.5%
a 124511
 
9.4%
r 97816
 
7.4%
i 97312
 
7.3%
e 88131
 
6.6%
n 86217
 
6.5%
p 68400
 
5.2%
d 56842
 
4.3%
l 46111
 
3.5%
Other values (27) 368808
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1326136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
152176
11.5%
o 139812
 
10.5%
a 124511
 
9.4%
r 97816
 
7.4%
i 97312
 
7.3%
e 88131
 
6.6%
n 86217
 
6.5%
p 68400
 
5.2%
d 56842
 
4.3%
l 46111
 
3.5%
Other values (27) 368808
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1326136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
152176
11.5%
o 139812
 
10.5%
a 124511
 
9.4%
r 97816
 
7.4%
i 97312
 
7.3%
e 88131
 
6.6%
n 86217
 
6.5%
p 68400
 
5.2%
d 56842
 
4.3%
l 46111
 
3.5%
Other values (27) 368808
27.8%

Posee Serv. Públicos Básicos
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
22710 
<No Aplica>
21883 
No
11714 

Length

Max length11
Median length2
Mean length5.4977356
Min length2

Characters and Unicode

Total characters309561
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th rowNo
5th row

Common Values

ValueCountFrequency (%)
22710
40.3%
<No Aplica> 21883
38.9%
No 11714
20.8%

Length

2025-02-08T12:32:09.944016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:32:10.049126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 33597
43.0%
22710
29.0%
aplica 21883
28.0%

Most occurring characters

ValueCountFrequency (%)
N 33597
10.9%
o 33597
10.9%
S 22710
 
7.3%
í 22710
 
7.3%
< 21883
 
7.1%
21883
 
7.1%
A 21883
 
7.1%
p 21883
 
7.1%
l 21883
 
7.1%
i 21883
 
7.1%
Other values (3) 65649
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 309561
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 33597
10.9%
o 33597
10.9%
S 22710
 
7.3%
í 22710
 
7.3%
< 21883
 
7.1%
21883
 
7.1%
A 21883
 
7.1%
p 21883
 
7.1%
l 21883
 
7.1%
i 21883
 
7.1%
Other values (3) 65649
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 309561
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 33597
10.9%
o 33597
10.9%
S 22710
 
7.3%
í 22710
 
7.3%
< 21883
 
7.1%
21883
 
7.1%
A 21883
 
7.1%
p 21883
 
7.1%
l 21883
 
7.1%
i 21883
 
7.1%
Other values (3) 65649
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 309561
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 33597
10.9%
o 33597
10.9%
S 22710
 
7.3%
í 22710
 
7.3%
< 21883
 
7.1%
21883
 
7.1%
A 21883
 
7.1%
p 21883
 
7.1%
l 21883
 
7.1%
i 21883
 
7.1%
Other values (3) 65649
21.2%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
S - SUBSIDIADO
26363 
<No Registra>
15906 
C - CONTRIBUTIVO
14038 

Length

Max length16
Median length14
Mean length14.216137
Min length13

Characters and Unicode

Total characters800468
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<No Registra>
2nd row<No Registra>
3rd row<No Registra>
4th row<No Registra>
5th row<No Registra>

Common Values

ValueCountFrequency (%)
S - SUBSIDIADO 26363
46.8%
<No Registra> 15906
28.2%
C - CONTRIBUTIVO 14038
24.9%

Length

2025-02-08T12:32:10.201142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:32:10.307438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
40401
26.4%
s 26363
17.2%
subsidiado 26363
17.2%
no 15906
 
10.4%
registra 15906
 
10.4%
c 14038
 
9.2%
contributivo 14038
 
9.2%

Most occurring characters

ValueCountFrequency (%)
96708
 
12.1%
I 80802
 
10.1%
S 79089
 
9.9%
O 54439
 
6.8%
D 52726
 
6.6%
- 40401
 
5.0%
U 40401
 
5.0%
B 40401
 
5.0%
N 29944
 
3.7%
R 29944
 
3.7%
Other values (14) 255613
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 800468
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
96708
 
12.1%
I 80802
 
10.1%
S 79089
 
9.9%
O 54439
 
6.8%
D 52726
 
6.6%
- 40401
 
5.0%
U 40401
 
5.0%
B 40401
 
5.0%
N 29944
 
3.7%
R 29944
 
3.7%
Other values (14) 255613
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 800468
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
96708
 
12.1%
I 80802
 
10.1%
S 79089
 
9.9%
O 54439
 
6.8%
D 52726
 
6.6%
- 40401
 
5.0%
U 40401
 
5.0%
B 40401
 
5.0%
N 29944
 
3.7%
R 29944
 
3.7%
Other values (14) 255613
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 800468
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
96708
 
12.1%
I 80802
 
10.1%
S 79089
 
9.9%
O 54439
 
6.8%
D 52726
 
6.6%
- 40401
 
5.0%
U 40401
 
5.0%
B 40401
 
5.0%
N 29944
 
3.7%
R 29944
 
3.7%
Other values (14) 255613
31.9%
Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
<No Aplica>
56058 
Discapacidad física
 
57
Persona Mayor
 
56
Discapacidad psicosocial o mental asociada a conductas adictivas
 
41
Enfermedad de alto costo
 
26
Other values (6)
 
69

Length

Max length67
Median length11
Mean length11.083027
Min length11

Characters and Unicode

Total characters624052
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPersona Mayor
2nd rowPersona Mayor
3rd rowDiscapacidad psicosocial o mental asociada a conductas adictivas
4th rowPersona Mayor
5th rowPersona Mayor

Common Values

ValueCountFrequency (%)
<No Aplica> 56058
99.6%
Discapacidad física 57
 
0.1%
Persona Mayor 56
 
0.1%
Discapacidad psicosocial o mental asociada a conductas adictivas 41
 
0.1%
Enfermedad de alto costo 26
 
< 0.1%
Discapacidad psicosocial o mental no asociada a conductas adictivas 20
 
< 0.1%
<No Registra> 16
 
< 0.1%
Discapacidad sensorial 12
 
< 0.1%
Discapacidad psicosocial o mental 8
 
< 0.1%
Discapacidad Múltiple 7
 
< 0.1%

Length

2025-02-08T12:32:10.466259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 56094
49.6%
aplica 56058
49.6%
discapacidad 151
 
0.1%
psicosocial 69
 
0.1%
o 69
 
0.1%
mental 69
 
0.1%
asociada 61
 
0.1%
adictivas 61
 
0.1%
conductas 61
 
0.1%
a 61
 
0.1%
Other values (11) 314
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a 57331
9.2%
c 56825
9.1%
i 56785
9.1%
56761
9.1%
o 56631
9.1%
p 56285
9.0%
l 56248
9.0%
N 56074
9.0%
> 56074
9.0%
< 56074
9.0%
Other values (21) 58964
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 624052
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 57331
9.2%
c 56825
9.1%
i 56785
9.1%
56761
9.1%
o 56631
9.1%
p 56285
9.0%
l 56248
9.0%
N 56074
9.0%
> 56074
9.0%
< 56074
9.0%
Other values (21) 58964
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 624052
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 57331
9.2%
c 56825
9.1%
i 56785
9.1%
56761
9.1%
o 56631
9.1%
p 56285
9.0%
l 56248
9.0%
N 56074
9.0%
> 56074
9.0%
< 56074
9.0%
Other values (21) 58964
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 624052
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 57331
9.2%
c 56825
9.1%
i 56785
9.1%
56761
9.1%
o 56631
9.1%
p 56285
9.0%
l 56248
9.0%
N 56074
9.0%
> 56074
9.0%
< 56074
9.0%
Other values (21) 58964
9.4%

FechaCorte
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
20241231
56307 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters450456
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20241231
2nd row20241231
3rd row20241231
4th row20241231
5th row20241231

Common Values

ValueCountFrequency (%)
20241231 56307
100.0%

Length

2025-02-08T12:32:10.611552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:32:10.696456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
20241231 56307
100.0%

Most occurring characters

ValueCountFrequency (%)
2 168921
37.5%
1 112614
25.0%
0 56307
 
12.5%
4 56307
 
12.5%
3 56307
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 450456
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 168921
37.5%
1 112614
25.0%
0 56307
 
12.5%
4 56307
 
12.5%
3 56307
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 450456
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 168921
37.5%
1 112614
25.0%
0 56307
 
12.5%
4 56307
 
12.5%
3 56307
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 450456
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 168921
37.5%
1 112614
25.0%
0 56307
 
12.5%
4 56307
 
12.5%
3 56307
 
12.5%

FechaActualizacion
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.0 KiB
20250131
56307 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters450456
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20250131
2nd row20250131
3rd row20250131
4th row20250131
5th row20250131

Common Values

ValueCountFrequency (%)
20250131 56307
100.0%

Length

2025-02-08T12:32:10.796078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T12:32:10.878775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
20250131 56307
100.0%

Most occurring characters

ValueCountFrequency (%)
2 112614
25.0%
0 112614
25.0%
1 112614
25.0%
5 56307
12.5%
3 56307
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 450456
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 112614
25.0%
0 112614
25.0%
1 112614
25.0%
5 56307
12.5%
3 56307
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 450456
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 112614
25.0%
0 112614
25.0%
1 112614
25.0%
5 56307
12.5%
3 56307
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 450456
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 112614
25.0%
0 112614
25.0%
1 112614
25.0%
5 56307
12.5%
3 56307
12.5%

Interactions

2025-02-08T12:31:55.269106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T12:31:52.571064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T12:31:53.721351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T12:31:54.559101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T12:31:55.433280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T12:31:52.849357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T12:31:54.026127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T12:31:54.743747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T12:31:55.600807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T12:31:53.164797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T12:31:54.206358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T12:31:54.921324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T12:31:55.771475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T12:31:53.459023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T12:31:54.393455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T12:31:55.109185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-08T12:32:11.026906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Año de Independización/IngresoAño desmovilizaciónBeneficioFABeneficioFPTBeneficioPDTBeneficioTRVClasificación Componente EspecíficoDepartamento de residenciaDepartamento de residencia descripciónDesagregadoDesembolsoBIEDesembolso BIEEstado ISUNEstado de la vinculación ASSEx GrupoGrupo EtarioIngresó/No ingresóLínea de FpT para el Máx. NivelMáximo Nivel FpT ReportadoNivel EducativoN° de HijosOcupacionEconomicaPosee Censo de Familia?Posee Censo de Habitabilidad?Posee Cónyuge o Compañero(a)?Posee Serv. Públicos BásicosPosee Servicio Social?Régimen de saludRégimen de tenencia ViviendaSexoSituación Final frente al procesoTipo de ASS VinculadaTipo de BIE AccedidoTipo de DesmovilizaciónTipo de ViviendaTotal Integrantes grupo familiar
Año de Independización/Ingreso1.0000.7550.0180.0100.0520.0500.0060.5380.5380.2410.1750.1810.2660.1190.0671.0000.2310.2310.4480.2230.4430.2870.2780.2870.2780.2610.0950.2780.1641.0000.2660.1760.0960.2780.213
Año desmovilización0.7551.0000.3180.1700.5100.5030.0180.1940.1940.2510.1340.2080.0790.3240.3690.1560.0710.0880.0850.1720.1800.2610.2710.1680.1970.1160.2410.1230.3640.2860.0700.1330.6760.1070.161
BeneficioFA0.0180.3181.0000.1660.3540.3640.0640.0700.0700.4170.0680.0680.0540.1840.2110.0180.0360.0600.0770.0770.2150.0630.0640.0710.0650.0540.1310.0680.1360.3960.0450.0680.0580.0650.020
BeneficioFPT0.0100.1700.1661.0000.2270.2320.0370.0560.0560.2790.0480.0480.0740.1450.1510.0100.1030.0750.0480.0480.1510.0410.0500.0450.0510.0740.0840.0500.0920.2540.0240.0480.0220.0510.012
BeneficioPDT0.0520.5100.3540.2271.0000.9720.1920.1320.1320.7600.0860.1010.0830.3090.3770.0520.1070.1320.1010.1950.3440.1670.0130.1790.0260.0820.3210.1270.3550.8540.0820.0870.1150.0920.074
BeneficioTRV0.0500.5030.3640.2320.9721.0000.1920.1320.1320.7750.0850.1000.0820.3040.3730.0500.1050.1300.0980.1880.3500.1610.0180.1740.0260.0810.3150.1240.3470.8730.0810.0860.1130.0900.072
Clasificación Componente Específico0.0060.0180.0640.0370.1920.1921.0000.0130.0130.1050.0350.0200.0460.0000.0690.0060.0060.0030.0190.0200.0610.0480.0320.0300.0240.0260.0440.0160.0410.1120.0140.0180.0070.0160.012
Departamento de residencia0.5380.1940.0700.0560.1320.1320.0131.0001.0000.1830.2770.2050.1390.1820.1330.5380.0890.0990.2190.1110.2070.3100.2990.1880.2820.2030.1870.1370.1900.2910.1670.1750.5250.1430.097
Departamento de residencia descripción0.5380.1940.0700.0560.1320.1320.0131.0001.0000.1830.2770.2050.1390.1820.1330.5380.0890.0990.2190.1110.2070.3100.2990.1880.2820.2030.1870.1370.1900.2910.1670.1750.5250.1430.097
DesagregadoDesembolsoBIE0.2410.2510.4170.2790.7600.7750.1050.1830.1831.0001.0000.5770.3720.1780.2060.2410.2150.2150.2840.2840.3680.5450.5490.3220.3950.5250.2930.2840.1940.6100.3610.5770.1650.2820.225
Desembolso BIE0.1750.1340.0680.0480.0860.0850.0350.2770.2771.0001.0001.0000.5890.1170.1210.1750.3660.3610.4290.4100.4590.3840.4250.3970.4300.5880.2640.4360.1650.6340.5731.0000.0890.4330.356
Estado ISUN0.1810.2080.0680.0480.1010.1000.0200.2050.2050.5771.0001.0000.3620.0750.0880.1810.2230.2270.2540.2830.3220.4640.4980.2750.3570.4420.1920.3010.1400.3880.3530.6030.1060.2960.242
Estado de la vinculación ASS0.2660.0790.0540.0740.0830.0820.0460.1390.1390.3720.5890.3621.0000.0530.0570.2660.1860.1860.2910.3020.3490.6660.7060.3870.4991.0000.2290.3170.1140.4210.4480.3410.0570.3170.237
Ex Grupo0.1190.3240.1840.1450.3090.3040.0000.1820.1820.1780.1170.0750.0531.0000.3290.1190.0510.0450.0720.0590.1210.1460.1420.0990.1010.0770.1260.0650.2750.1780.0440.0820.8990.0480.033
Grupo Etario0.0670.3690.2110.1510.3770.3730.0690.1330.1330.2060.1210.0880.0570.3291.0000.0670.0650.0600.0440.0870.3060.1050.1340.0920.0970.0730.1690.0920.2130.2150.0470.0710.3800.0790.043
Ingresó/No ingresó1.0000.1560.0180.0100.0520.0500.0060.5380.5380.2410.1750.1810.2660.1190.0671.0000.2310.2310.4480.2860.4430.2870.2780.2870.2780.2610.0950.2780.1641.0000.2660.1760.0960.2780.196
Línea de FpT para el Máx. Nivel0.2310.0710.0360.1030.1070.1050.0060.0890.0890.2150.3660.2230.1860.0510.0650.2311.0000.3890.3200.1070.1720.3040.3240.1810.2530.2940.1330.1090.1980.2210.1740.2150.1610.1080.111
Máximo Nivel FpT Reportado0.2310.0880.0600.0750.1320.1300.0030.0990.0990.2150.3610.2270.1860.0450.0600.2310.3891.0000.3700.1100.1730.3120.3290.1830.2470.2940.1410.1120.1420.2250.1720.2120.1230.1050.112
Nivel Educativo0.4480.0850.0770.0480.1010.0980.0190.2190.2190.2840.4290.2540.2910.0720.0440.4480.3200.3701.0000.2510.2700.4960.5030.2880.3590.4060.1580.2530.1380.3390.2890.2490.1130.2550.193
N° de Hijos0.2230.1720.0770.0480.1950.1880.0200.1110.1110.2840.4100.2830.3020.0590.0870.2860.1070.1100.2511.0000.3920.9970.8560.6490.6070.4690.1330.2880.2190.3360.2750.2390.1290.2890.965
OcupacionEconomica0.4430.1800.2150.1510.3440.3500.0610.2070.2070.3680.4590.3220.3490.1210.3060.4430.1720.1730.2700.3921.0000.7810.7960.4550.5630.4860.1970.3990.1990.4370.3460.2670.1220.3990.281
Posee Censo de Familia?0.2870.2610.0630.0410.1670.1610.0480.3100.3100.5450.3840.4640.6660.1460.1050.2870.3040.3120.4960.9970.7811.0000.8561.0000.8560.6530.1440.8560.3310.6520.6660.3870.1160.8560.684
Posee Censo de Habitabilidad?0.2780.2710.0640.0500.0130.0180.0320.2990.2990.5490.4250.4980.7060.1420.1340.2780.3240.3290.5030.8560.7960.8561.0000.8571.0000.6930.2271.0000.2550.6430.7050.4290.0741.0000.628
Posee Cónyuge o Compañero(a)?0.2870.1680.0710.0450.1790.1740.0300.1880.1880.3220.3970.2750.3870.0990.0920.2870.1810.1830.2880.6490.4551.0000.8571.0000.6070.4650.1200.4970.2010.3830.3870.2310.1350.5000.487
Posee Serv. Públicos Básicos0.2780.1970.0650.0510.0260.0260.0240.2820.2820.3950.4300.3570.4990.1010.0970.2780.2530.2470.3590.6070.5630.8561.0000.6071.0000.4900.1910.7540.1810.4550.5030.3080.0770.7570.446
Posee Servicio Social?0.2610.1160.0540.0740.0820.0810.0260.2030.2030.5250.5880.4421.0000.0770.0730.2610.2940.2940.4060.4690.4860.6530.6930.4650.4901.0000.2270.4920.1360.5930.6930.4170.0370.4910.371
Régimen de salud0.0950.2410.1310.0840.3210.3150.0440.1870.1870.2930.2640.1920.2290.1260.1690.0950.1330.1410.1580.1330.1970.1440.2270.1200.1910.2271.0000.1980.1470.3270.2320.1920.0960.1900.118
Régimen de tenencia Vivienda0.2780.1230.0680.0500.1270.1240.0160.1370.1370.2840.4360.3010.3170.0650.0920.2780.1090.1120.2530.2880.3990.8561.0000.4970.7540.4920.1981.0000.1870.3280.2910.2610.1670.3750.260
Sexo0.1640.3640.1360.0920.3550.3470.0410.1900.1900.1940.1650.1400.1140.2750.2130.1640.1980.1420.1380.2190.1990.3310.2550.2010.1810.1360.1470.1871.0000.2410.1200.0990.4250.1650.133
Situación Final frente al proceso1.0000.2860.3960.2540.8540.8730.1120.2910.2910.6100.6340.3880.4210.1780.2151.0000.2210.2250.3390.3360.4370.6520.6430.3830.4550.5930.3270.3280.2411.0000.4070.3670.1520.3270.259
Tipo de ASS Vinculada0.2660.0700.0450.0240.0820.0810.0140.1670.1670.3610.5730.3530.4480.0440.0470.2660.1740.1720.2890.2750.3460.6660.7050.3870.5030.6930.2320.2910.1200.4071.0000.3330.0670.2920.216
Tipo de BIE Accedido0.1760.1330.0680.0480.0870.0860.0180.1750.1750.5771.0000.6030.3410.0820.0710.1760.2150.2120.2490.2390.2670.3870.4290.2310.3080.4170.1920.2610.0990.3670.3331.0000.1380.2530.207
Tipo de Desmovilización0.0960.6760.0580.0220.1150.1130.0070.5250.5250.1650.0890.1060.0570.8990.3800.0960.1610.1230.1130.1290.1220.1160.0740.1350.0770.0370.0960.1670.4250.1520.0670.1381.0000.1020.046
Tipo de Vivienda0.2780.1070.0650.0510.0920.0900.0160.1430.1430.2820.4330.2960.3170.0480.0790.2780.1080.1050.2550.2890.3990.8561.0000.5000.7570.4910.1900.3750.1650.3270.2920.2530.1021.0000.263
Total Integrantes grupo familiar0.2130.1610.0200.0120.0740.0720.0120.0970.0970.2250.3560.2420.2370.0330.0430.1960.1110.1120.1930.9650.2810.6840.6280.4870.4460.3710.1180.2600.1330.2590.2160.2070.0460.2631.000

Missing values

2025-02-08T12:31:56.247073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-08T12:31:56.892322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Tipo de DesmovilizaciónEx GrupoAño desmovilizaciónIngresó/No ingresóAño de Independización/IngresoGrupo EtarioSexoSituación Final frente al procesoDepartamento de residencia descripciónMunicipio de residencia descripciónDepartamento de residenciaMunicipio de residenciaBeneficioTRVBeneficioFABeneficioFPTBeneficioPDTNivel EducativoMáximo Nivel FpT ReportadoLínea de FpT para el Máx. NivelOcupacionEconomicaDesembolso BIETipo de BIE AccedidoDesagregadoDesembolsoBIEEstado ISUNPosee Servicio Social?Estado de la vinculación ASSTipo de ASS VinculadaPosee Censo de Familia?Posee Cónyuge o Compañero(a)?N° de HijosTotal Integrantes grupo familiarPosee Censo de Habitabilidad?Tipo de ViviendaRégimen de tenencia ViviendaPosee Serv. Públicos BásicosRégimen de saludClasificación Componente EspecíficoFechaCorteFechaActualizacion
0IndividualELN20172017Mayor de 60 añosMasculinoEn ProcesoBogotá D.C.Bogotá1111001NoNoBachiller<No Aplica><No Aplica>Población Económicamente InactivaNo<No Aplica>No posee desembolso BIE<No Aplica>Se encuentra vinculado a Servicio SocialVinculadoMultiplicadores del ConocimientoNo01ApartamentoPropia, la están pagando<No Registra>Persona Mayor2024123120250131
1ColectivaAUC20062006Mayor de 60 añosMasculinoEn ProcesoCesarLa Jagua De Ibirico2020400NoNoBásica Primaria<No Aplica><No Aplica>Población Económicamente InactivaNo<No Aplica>No posee desembolso BIE<No Aplica>Posee Certificación de Servicio SocialCertificadoRecuperación Ambiental46CasaEn arriendo o subarriendo<No Registra>Persona Mayor2024123120250131
2ColectivaAUC20042004Entre 41 y 60 añosMASCULINOFuera del ProcesoValle del CaucaPradera7676563NoNoNoNoBásica Primaria<No Aplica><No Aplica>Población Económicamente InactivaNo<No Aplica>No está en Proceso<No Aplica>Posee Certificación de Servicio SocialCertificadoEmbellecimiento de Espacio PublicoNo02Casa-LotePropia, totalmente pagada<No Registra>Discapacidad psicosocial o mental asociada a conductas adictivas2024123120250131
3IndividualFARC20172017Mayor de 60 añosMasculinoEn ProcesoMetaVista Hermosa5050711NoNoBásica Primaria<No Aplica><No Aplica>Población Económicamente InactivaNo<No Aplica>No posee desembolso BIE<No Aplica>Posee Certificación de Servicio SocialCertificadoEmbellecimiento de Espacio PublicoNo02CasaCon permiso del propietario, sin pago algunoNo<No Registra>Persona Mayor2024123120250131
4IndividualFARC20152015Mayor de 60 añosMasculinoEn ProcesoCaquetáCartagena Del Chairá1818150NoNoPor EstablecerOperarioAGROPECUARIAPoblación Económicamente InactivaNo<No Aplica>No posee desembolso BIE<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>No01Cuarto(s)Con permiso del propietario, sin pago alguno<No Registra>Persona Mayor2024123120250131
5ColectivaAUC20062006Entre 41 y 60 añosMASCULINOFuera del ProcesoBogotá D.C.Bogotá1111001NoNoNoNoBásica Secundaria<No Aplica><No Aplica>DesocupadosNo<No Aplica>No está en Proceso<No Aplica>Posee Certificación de Servicio SocialCertificadoRecuperación AmbientalNo01HabitaciónEn arriendo o subarriendoNo<No Registra>Discapacidad psicosocial o mental asociada a conductas adictivas2024123120250131
6ColectivaAUC20062006Entre 41 y 60 añosMasculinoAusente del procesoNorte de SantanderCúcuta5454001NoNoNoNoBásica SecundariaTécnico<No Registra>DesocupadosNo<No Aplica>No está en Proceso<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>No01CasaEn arriendo o subarriendo<No Registra>Discapacidad psicosocial o mental asociada a conductas adictivas2024123120250131
7IndividualFARC20182018Mayor de 60 añosMasculinoEn ProcesoMetaUribe5050370NoNoBásica Primaria<No Aplica><No Aplica>Población Económicamente InactivaNo<No Aplica>No posee desembolso BIE<No Aplica>Posee Certificación de Servicio SocialCertificadoEmbellecimiento de Espacio PublicoNo01CasaCon permiso del propietario, sin pago alguno<No Registra>Persona Mayor2024123120250131
8IndividualFARC20172017Mayor de 60 añosMasculinoEn ProcesoCundinamarcaTibacuy2525805NoNoBachillerAuxiliarMERCADEO Y VENTASPoblación Económicamente InactivaPlan de NegocioPosee desembolso BIEEn FuncionamientoNo está vinculado a Servicio Social<No Aplica><No Aplica>02CasaCon permiso del propietario, sin pago algunoNo<No Registra>Persona Mayor2024123120250131
9IndividualFARC20142014Entre 26 y 40 añosFemeninoAusente del procesoAntioquiaNechí0505495NoNoNoNoBásica Primaria<No Aplica><No Aplica>DesocupadosPlan de NegocioPosee desembolso BIECerradoPosee Certificación de Servicio SocialCertificadoEmbellecimiento de Espacio PublicoNo23Vivienda (casa) indígenaPosesión sin título (ocupante de hecho) o propiedad colectivaNoS - SUBSIDIADOEnfermedad de alto costo2024123120250131
Tipo de DesmovilizaciónEx GrupoAño desmovilizaciónIngresó/No ingresóAño de Independización/IngresoGrupo EtarioSexoSituación Final frente al procesoDepartamento de residencia descripciónMunicipio de residencia descripciónDepartamento de residenciaMunicipio de residenciaBeneficioTRVBeneficioFABeneficioFPTBeneficioPDTNivel EducativoMáximo Nivel FpT ReportadoLínea de FpT para el Máx. NivelOcupacionEconomicaDesembolso BIETipo de BIE AccedidoDesagregadoDesembolsoBIEEstado ISUNPosee Servicio Social?Estado de la vinculación ASSTipo de ASS VinculadaPosee Censo de Familia?Posee Cónyuge o Compañero(a)?N° de HijosTotal Integrantes grupo familiarPosee Censo de Habitabilidad?Tipo de ViviendaRégimen de tenencia ViviendaPosee Serv. Públicos BásicosRégimen de saludClasificación Componente EspecíficoFechaCorteFechaActualizacion
56297ColectivaAUC20062016Entre 41 y 60 añosMasculinoCulminadoAntioquiaRemedios0505604NoNoNoNoBásica SecundariaComplementarioOTROSOcupados en el sector InformalNo<No Aplica>Culminado con agotamiento de tiempo para acceder a BIE<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>No06CasaCon permiso del propietario, sin pago algunoNoS - SUBSIDIADO<No Aplica>2024123120250131
56298ColectivaAUC20042015Entre 41 y 60 añosMasculinoCulminadoAntioquiaItagui0505360NoNoNoNoBachillerTécnicoALIMENTOS Y BEBIDASOcupados en el sector InformalNo<No Aplica>Culminado con agotamiento de tiempo para acceder a BIE<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>14CasaCon permiso del propietario, sin pago algunoS - SUBSIDIADO<No Aplica>2024123120250131
56299ColectivaAUC20062015Mayor de 60 añosMasculinoCulminadoSantanderConfines6868209NoNoNoNoBachiller<No Aplica><No Aplica>Población Económicamente InactivaNo<No Aplica>Culminado con agotamiento de tiempo para acceder a BIE<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>24CasaPropia, totalmente pagadaNoS - SUBSIDIADO<No Aplica>2024123120250131
56300ColectivaAUC20062016Entre 41 y 60 añosMasculinoFuera del ProcesoSantanderFloridablanca6868276NoNoNoNoPor Establecer<No Aplica><No Aplica>No AplicaNo<No Aplica>No está en Proceso<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>No<No Aplica>-2-1No<No Aplica><No Aplica><No Aplica>S - SUBSIDIADO<No Aplica>2024123120250131
56301ColectivaAUC20062015Entre 41 y 60 añosMASCULINOFuera del ProcesoAntioquiaBello0505088NoNoNoNoBachiller<No Aplica><No Aplica>No AplicaNo<No Aplica>No está en Proceso<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>No<No Aplica>-2-1No<No Aplica><No Aplica><No Aplica>S - SUBSIDIADO<No Aplica>2024123120250131
56302ColectivaAUC20062019Entre 41 y 60 añosFemeninoEn ProcesoNorte de SantanderEl Zulia5454261NoNoBachillerTécnicoSERVICIOSDesocupadosNo<No Aplica>No posee desembolso BIE<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>24No<No Aplica><No Aplica><No Aplica>S - SUBSIDIADO<No Aplica>2024123120250131
56303ColectivaAUC20062017Entre 41 y 60 añosMasculinoEn ProcesoAtlánticoBarranquilla0808001NoNoBásica Primaria<No Aplica><No Aplica>Ocupados en el sector InformalNo<No Aplica>No posee desembolso BIE<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>03ApartamentoEn arriendo o subarriendo<No Registra>Discapacidad Cognitiva2024123120250131
56304ColectivaAUC20122020Entre 41 y 60 añosMasculinoEn ProcesoAtlánticoSoledad0808758NoNoBásica SecundariaComplementarioFINANZAS Y ADMINISTRACIONDesocupadosNo<No Aplica>No posee desembolso BIE<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>02No<No Aplica><No Aplica><No Aplica><No Registra>Discapacidad Cognitiva2024123120250131
56305ColectivaAUC20062017Mayor de 60 añosMasculinoEn ProcesoSantanderPiedecuesta6868547NoNoBásica Primaria<No Aplica><No Aplica>Población Económicamente InactivaNo<No Aplica>No posee desembolso BIE<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>02ApartamentoEn arriendo o subarriendoC - CONTRIBUTIVOEnfermedad de alto costo2024123120250131
56306ColectivaAUC20052019Entre 41 y 60 añosMasculinoEn ProcesoAtlánticoBarranquilla0808001NoNoBachillerTécnicoMECANICA AUTOMOTRIZ Y DE MOTOSDesocupadosNo<No Aplica>No posee desembolso BIE<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>No13No<No Aplica><No Aplica><No Aplica>C - CONTRIBUTIVOEnfermedad de alto costo2024123120250131

Duplicate rows

Most frequently occurring

Tipo de DesmovilizaciónEx GrupoAño desmovilizaciónIngresó/No ingresóAño de Independización/IngresoGrupo EtarioSexoSituación Final frente al procesoDepartamento de residencia descripciónMunicipio de residencia descripciónDepartamento de residenciaMunicipio de residenciaBeneficioTRVBeneficioFABeneficioFPTBeneficioPDTNivel EducativoMáximo Nivel FpT ReportadoLínea de FpT para el Máx. NivelOcupacionEconomicaDesembolso BIETipo de BIE AccedidoDesagregadoDesembolsoBIEEstado ISUNPosee Servicio Social?Estado de la vinculación ASSTipo de ASS VinculadaPosee Censo de Familia?Posee Cónyuge o Compañero(a)?N° de HijosTotal Integrantes grupo familiarPosee Censo de Habitabilidad?Tipo de ViviendaRégimen de tenencia ViviendaPosee Serv. Públicos BásicosRégimen de saludClasificación Componente EspecíficoFechaCorteFechaActualizacion# duplicates
541ColectivaAUC2006No-1Entre 41 y 60 añosMASCULINONo ha ingresado<No Registra><No Registra><No Registra><No Registra>NoNoNoNoPor Establecer<No Aplica><No Aplica>No AplicaNo<No Aplica>No está en Proceso<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>No<No Aplica>-2-1No<No Aplica><No Aplica><No Aplica>S - SUBSIDIADO<No Aplica>2024123120250131165
144ColectivaAUC2005No-1Entre 41 y 60 añosMASCULINONo ha ingresado<No Registra><No Registra><No Registra><No Registra>NoNoNoNoPor Establecer<No Aplica><No Aplica>No AplicaNo<No Aplica>No está en Proceso<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>No<No Aplica>-2-1No<No Aplica><No Aplica><No Aplica><No Registra><No Aplica>2024123120250131129
146ColectivaAUC2005No-1Entre 41 y 60 añosMASCULINONo ha ingresado<No Registra><No Registra><No Registra><No Registra>NoNoNoNoPor Establecer<No Aplica><No Aplica>No AplicaNo<No Aplica>No está en Proceso<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>No<No Aplica>-2-1No<No Aplica><No Aplica><No Aplica>S - SUBSIDIADO<No Aplica>2024123120250131108
539ColectivaAUC2006No-1Entre 41 y 60 añosMASCULINONo ha ingresado<No Registra><No Registra><No Registra><No Registra>NoNoNoNoPor Establecer<No Aplica><No Aplica>No AplicaNo<No Aplica>No está en Proceso<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>No<No Aplica>-2-1No<No Aplica><No Aplica><No Aplica><No Registra><No Aplica>202412312025013194
61ColectivaAUC2004No-1Entre 41 y 60 añosMASCULINONo ha ingresado<No Registra><No Registra><No Registra><No Registra>NoNoNoNoPor Establecer<No Aplica><No Aplica>No AplicaNo<No Aplica>No está en Proceso<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>No<No Aplica>-2-1No<No Aplica><No Aplica><No Aplica>S - SUBSIDIADO<No Aplica>202412312025013186
59ColectivaAUC2004No-1Entre 41 y 60 añosMASCULINONo ha ingresado<No Registra><No Registra><No Registra><No Registra>NoNoNoNoPor Establecer<No Aplica><No Aplica>No AplicaNo<No Aplica>No está en Proceso<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>No<No Aplica>-2-1No<No Aplica><No Aplica><No Aplica><No Registra><No Aplica>202412312025013151
390ColectivaAUC20052005Entre 41 y 60 añosMASCULINOFuera del ProcesoAntioquiaMedellín0505001NoNoNoNoPor Establecer<No Aplica><No Aplica>No AplicaNo<No Aplica>No está en Proceso<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>No<No Aplica>-2-1No<No Aplica><No Aplica><No Aplica>S - SUBSIDIADO<No Aplica>202412312025013138
761ColectivaAUC20062006Entre 41 y 60 añosMASCULINOFuera del Proceso<No Registra><No Registra><No Registra><No Registra>NoNoNoNoPor Establecer<No Aplica><No Aplica>No AplicaNo<No Aplica>No está en Proceso<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>No<No Aplica>-2-1No<No Aplica><No Aplica><No Aplica>S - SUBSIDIADO<No Aplica>202412312025013134
302ColectivaAUC20052005Entre 41 y 60 añosMASCULINOFuera del Proceso<No Registra><No Registra><No Registra><No Registra>NoNoNoNoPor Establecer<No Aplica><No Aplica>No AplicaNo<No Aplica>No está en Proceso<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>No<No Aplica>-2-1No<No Aplica><No Aplica><No Aplica>S - SUBSIDIADO<No Aplica>202412312025013132
384ColectivaAUC20052005Entre 41 y 60 añosMASCULINOFuera del ProcesoAntioquiaMedellín0505001NoNoNoNoPor Establecer<No Aplica><No Aplica><No Registra>No<No Aplica>No está en Proceso<No Aplica>No está vinculado a Servicio Social<No Aplica><No Aplica>No<No Aplica>-2-1No<No Aplica><No Aplica><No Aplica><No Registra><No Aplica>202412312025013131